Ultrasonographic findings of early abortion: suggested predictors

International Nuclear Information System (INIS)

Jun, Soon Ae; Ahn, Myoung Ock; Cha, Kwang Yul; Lee, Young Doo

1992-01-01

To investigate predictable ultrasonographic findings of early abortion. To investigate objective rules for the screening of abortion. Ultrasonographic examination of 111 early pregnancies between the sixth and ninth week in women who had regular 28 day menstrual cycles was performed. Ultrasonographic measurements of the gestational sac, crown rump length and fetal heart rate were performed using a linear array real time transducer with doppler ultrasonogram. All measurements of 17 early abortions were compared to those of 94 normal pregnancies. Most of early aborted pregnancies were classified correctly by discriminant analysis with G-SAC and CRL (G-SAC=0.5 CRL + 15, sensitivity 76.5%, specificity 96.8%). With the addition of FHR, 94.1% of early abortions could be predicted. In conclusion, ultrasonographic findings of early intrauterine growth retardation, small gestational sac and bradycardia can be predictable signs suggestive of poor prognosis of early pregnancies

Noninvasive detection of hepatic lipidosis in dairy cows with calibrated ultrasonographic image analysis.

Science.gov (United States)

Starke, A; Haudum, A; Weijers, G; Herzog, K; Wohlsein, P; Beyerbach, M; de Korte, C L; Thijssen, J M; Rehage, J

2010-07-01

The aim was to test the accuracy of calibrated digital analysis of ultrasonographic hepatic images for diagnosing fatty liver in dairy cows. Digital analysis was performed by means of a novel method, computer-aided ultrasound diagnosis (CAUS), previously published by the authors. This method implies a set of pre- and postprocessing steps to normalize and correct the transcutaneous ultrasonographic images. Transcutaneous hepatic ultrasonography was performed before surgical correction on 151 German Holstein dairy cows (mean +/- standard error of the means; body weight: 571+/-7 kg; age: 4.9+/-0.2 yr; DIM: 35+/-5) with left-sided abomasal displacement. Concentration of triacylglycerol (TAG) was biochemically determined in liver samples collected via biopsy and values were considered the gold standard to which ultrasound estimates were compared. According to histopathologic examination of biopsies, none of the cows suffered from hepatic disorders other than hepatic lipidosis. Hepatic TAG concentrations ranged from 4.6 to 292.4 mg/g of liver fresh weight (FW). High correlations were found between the hepatic TAG and mean echo level (r=0.59) and residual attenuation (ResAtt; r=0.80) obtained in ultrasonographic imaging. High correlation existed between ResAtt and mean echo level (r=0.76). The 151 studied cows were split randomly into a training set of 76 cows and a test set of 75 cows. Based on the data from the training set, ResAtt was statistically selected by means of stepwise multiple regression analysis for hepatic TAG prediction (R(2)=0.69). Then, using the predicted TAG data of the test set, receiver operating characteristic analysis was performed to summarize the accuracy and predictive potential of the differentiation between various measured hepatic TAG values, based on TAG predicted from the regression formula. The area under the curve values of the receiver operating characteristic based on the regression equation were 0.94 (or=50mg of TAG/g of FW), 0.83 (or

Ultrasonographic features of intestinal adenocarcinoma in five cats

International Nuclear Information System (INIS)

Rivers, B.J.; Walter, P.A.; Feeney, D.A.; Johnston, G.R.

1997-01-01

Adenocarcinoma, followed by lymphosarcoma, are the most common feline intestinal neoplasms. Clinicopathological, survey radiographic, and ultrasonographic findings of five cats with intestinal adenocarcinoma are reported. An abdominal mass was palpable in all five cats, but the mass could be localized to bowel in only two cats. Radiographically an abdominal mass was detected in only one cat. Ultrasonographically there was a segmental intestinal mural mass in all five cats. The mass was characterized by circumferential bowel wall thickening with transmural loss of normal sonographic wall layers. In one cat, the circumferential symmetric hypoechoic bowel wall thickening was similar to that reported for segmental lymphoma. In the other four cats, the sonographic features of the thickened bowel wall were varied, being mixed echogenicity and asymmetric in 3 cats and mixed echogenicity and symmetric in one. The results of the present report suggest that sonographic observation of mixed echogenicity segmental intestinal wall thickening in the cat represents adenocarcinoma rather than lymphosarcoma, although other infiltrative diseases should be considered

Ultrasonographic and clinical findings of inguinal hernia containing the ovary or omentum in girls

Energy Technology Data Exchange (ETDEWEB)

Shin, Su Mi; Chai, Jee Won [Dept. of Radiology, SMG-SNU Boramae Medical Center, Seoul (Korea, Republic of)

2016-09-15

To characterize the ultrasonographic and clinical findings of inguinal hernia containing the ovary or omentum in girls. We studied 46 girls (49 cases) who were diagnosed with inguinal hernia on ultrasonography between March 2009 and December 2015. The ultrasonographic findings were retrospectively analyzed with respect to location, age at detection, contents of hernia, diameter of the canal of Nuck, and incidence of reducibility, incarceration and strangulation. The clinical findings included the number of cases that underwent operation, contents of hernia discovered during operation, and duration between ultrasonographic diagnosis and operation. The two groups in which inguinal hernia contained the ovary and omentum were statistically compared. Of the 49 cases, the contents of hernia were the ovary or tube in 14 cases, omentum in 32 cases, and bowel in 3 cases. The ovarian herniation group was significantly younger (10.1 months vs. 4.9 years, p < 0.001), had a lower incidence of reducibility (n = 3 vs. n = 29, p < 0.001), higher incidence of incarceration (n = 4 vs. n = 0, p = 0.006), and a shorter duration between ultrasonographic diagnosis and operation (5.7 days vs. 55.8 days, p = 0.032) than the omental herniation group. The ovarian herniation group was younger, had a lower incidence of reducibility, higher incidence of incarceration, and a shorter duration between ultrasonographic diagnosis and operation.

Ultrasonographic and clinical findings of inguinal hernia containing the ovary or omentum in girls

International Nuclear Information System (INIS)

Shin, Su Mi; Chai, Jee Won

2016-01-01

To characterize the ultrasonographic and clinical findings of inguinal hernia containing the ovary or omentum in girls. We studied 46 girls (49 cases) who were diagnosed with inguinal hernia on ultrasonography between March 2009 and December 2015. The ultrasonographic findings were retrospectively analyzed with respect to location, age at detection, contents of hernia, diameter of the canal of Nuck, and incidence of reducibility, incarceration and strangulation. The clinical findings included the number of cases that underwent operation, contents of hernia discovered during operation, and duration between ultrasonographic diagnosis and operation. The two groups in which inguinal hernia contained the ovary and omentum were statistically compared. Of the 49 cases, the contents of hernia were the ovary or tube in 14 cases, omentum in 32 cases, and bowel in 3 cases. The ovarian herniation group was significantly younger (10.1 months vs. 4.9 years, p < 0.001), had a lower incidence of reducibility (n = 3 vs. n = 29, p < 0.001), higher incidence of incarceration (n = 4 vs. n = 0, p = 0.006), and a shorter duration between ultrasonographic diagnosis and operation (5.7 days vs. 55.8 days, p = 0.032) than the omental herniation group. The ovarian herniation group was younger, had a lower incidence of reducibility, higher incidence of incarceration, and a shorter duration between ultrasonographic diagnosis and operation

Ultrasonographic and CT findings of hepatosplenic tuberculosis

International Nuclear Information System (INIS)

Moon, Un Hyeon; Lee, Jeong Seok; Ko, Kang Seok; Park, Byung Ran; Yang, Dong Cheol; Im, Ju Hyeon; Kang, In Young

1998-01-01

To evaluate the ultrasonographic and CT findings of hepatosplenic tuberculosis Materials and Methods: We retrospectively reviewed the ultrasonographic and CT findings of confirmed hepatosplenic tuberculosis in 12 patients. Six were men and six were women ; their average age was 41, and most were in their twenties. Lesions of the liver and spleen, as well as associated findings such as abdominal tuberculosis and other organ involvement of tuberculosis were analyzed. Results : There were three cases of hepatic tuberculosis, seven of splenic tuberculosis, and two of hepatosplenic involvement of tuberculosis. On the basis of the ultrasonographic and CT findings, hepatosplenic tuberculosis was classified as one of two patterns : miliary or micronodular, ormacronodular. The micronodular type was more common (9/12 cases) being characterized by innumerable micronodules,and with easy coalescence in the liver and spleen in five of the nine cases. The macronodular type of low density mass was noted in the other three patients. Splenomegaly was noted in 12 cases and hepatomegaly in ten. Pulmonary tuberculosis-including the miliary type(n=5)-was noted in eight patients. Associated abdominal tuberculosis such as lymphadenopathy with central low density and peripheral rim enhancement (n=6), tuberculous peritonitis(n=3),highly attenuated ascites(n=6), adrenal tuberculosis(n=1), renal tuberculosis(n=1), ovarian abscess(n=1), psoasabscess(n=1), and systemic tuberculosis such as central nervous system tuberculoma(n=2), cervical lymphadenopathy(n=4) and tuberculous spondylitis(n=1) were noted. Conclusion : Ultrasonography and CT were valuable in the detection and diagnosis of hepatosplenic tuberculosis

Ultrasonographic and CT findings of hepatosplenic tuberculosis

Energy Technology Data Exchange (ETDEWEB)

Moon, Un Hyeon; Lee, Jeong Seok; Ko, Kang Seok; Park, Byung Ran; Yang, Dong Cheol; Im, Ju Hyeon [Kwangju Christian Hospital, Kwangju (Korea, Republic of); Kang, In Young [Kwangju Green Cross Hospital, Kwangju (Korea, Republic of)

1998-08-01

To evaluate the ultrasonographic and CT findings of hepatosplenic tuberculosis Materials and Methods: We retrospectively reviewed the ultrasonographic and CT findings of confirmed hepatosplenic tuberculosis in 12 patients. Six were men and six were women ; their average age was 41, and most were in their twenties. Lesions of the liver and spleen, as well as associated findings such as abdominal tuberculosis and other organ involvement of tuberculosis were analyzed. Results : There were three cases of hepatic tuberculosis, seven of splenic tuberculosis, and two of hepatosplenic involvement of tuberculosis. On the basis of the ultrasonographic and CT findings, hepatosplenic tuberculosis was classified as one of two patterns : miliary or micronodular, ormacronodular. The micronodular type was more common (9/12 cases) being characterized by innumerable micronodules,and with easy coalescence in the liver and spleen in five of the nine cases. The macronodular type of low density mass was noted in the other three patients. Splenomegaly was noted in 12 cases and hepatomegaly in ten. Pulmonary tuberculosis-including the miliary type(n=5)-was noted in eight patients. Associated abdominal tuberculosis such as lymphadenopathy with central low density and peripheral rim enhancement (n=6), tuberculous peritonitis(n=3),highly attenuated ascites(n=6), adrenal tuberculosis(n=1), renal tuberculosis(n=1), ovarian abscess(n=1), psoasabscess(n=1), and systemic tuberculosis such as central nervous system tuberculoma(n=2), cervical lymphadenopathy(n=4) and tuberculous spondylitis(n=1) were noted. Conclusion : Ultrasonography and CT were valuable in the detection and diagnosis of hepatosplenic tuberculosis.

Ultrasonographic finding of hepatocellular carcinoma

Energy Technology Data Exchange (ETDEWEB)

Ryu, Han Soo; Woo, Seong Ku; Lim, Jae Hoon; Ko, Young Tae; Kim, Ho Kyun; Kim, Soon Yong [Kyung Hee University Hospital, Seoul (Korea, Republic of)

1983-12-15

With the development of gray scale ultrasonography, detection and evaluation of hepatic parenchymal disease including space occupying lesion are easily performed and frequently used in the world. Thrity five cases of histopathologically proven and ultrasonographically suggested hepatocellular carcinoma are retrospectively studied. The results were as follows; 1. Ultrasonographic findings of hepatocellular carcinoma show hyperechoic pattern in 22 cases (63%), hypoechoic pattern in 2 cases (6%), and mixed pattern in 11 cases (31%). 2. The margin of tumor is ill-defined in 19 cases (54%) and well defined in16 cases (46%). 3. The size of tumor by sonographic measurement was large than 5 cm in diameter in 33 cases (94%). 4. The number of tumor is solitary in 19 cases and multiple in 16 cases. The sites of involved lobe were right lobe in 22 cases (63%), left lobe in 2 cases (6%), and both lobes in 11 cases (31%). 5. Associated sonographic findings were hepatomegaly with focal contour change in 25 cases (71%), splenomegaly in 16 cases (46%), cirrhosis of liver in 15 cases (43%), ascites in 11 cases (31%) and tumoral thrombosis in portal vein in 8 cases (23%). 6. The sex ratio is 6 : 1 male predominence and the age ranges from 32 to 76 years with highest incidence in 5th and 6th decades.

Ultrasonographic and mammographic findings of gynecomastia

Energy Technology Data Exchange (ETDEWEB)

Lee, Soo Kyung; Choi, Gyo Chang; Hong, Hyun Sook; Kim, Young Beom; Lee, Hae Kyung; Kwon, Kui Hyang [Soonchunhyang Univ. College of Medicine, Asan (Korea, Republic of)

1996-11-01

The purpose of this study is to evaluate the radiologic features and clinical utility of ultrasonography and mammography in cases of gynecomastia. This study involved 40 men in whom gynecomastia had been pathologically diagnosed by surgical incision. In 21 cases, a retrospective analysis of ultrasonographic and mammographic findings was performed. Causative factors of gynecomastia among the 40 pathologically-proven cases were idiopathic or pubertal in 33 cases, related to male hormone deficiency in three cases and to chronic liver disease in four. Bi-lateral involvement was seen in 14 cases, and unilateral involvement in 26;among unilateral cases, right side was involved in 10 cases, and the left side in 16. Mammographically, a subareolar discoid lesion was present in 12 cases, diffuse increased breast density was seen in five cases and dendritic marginated subareolar lesion without microcalcification in one. Ultrasonographically, a round smooth marginated low echogenic lesion in the subareolar region was seen in five cases, a diffuse hyperechogenic pattern without definite mass in two cases and an ill defined low echogenic lesion in one. The male breast is small, so in cases of gynecomastia, ultrasonography is an effective diagnostic modality. Mamography will, however, be helpful in the detection of microcalcification in cases of gynecomastia seen on sonography.

Ultrasonographic Findings of Papillary Thyroid Cancer with or without Hashimoto's Thyroiditis

Energy Technology Data Exchange (ETDEWEB)

Park, Jun Young; Lee, Tae Hyun; Park, Dong Hee [Korea Cancer Center Hospital, Seoul (Korea, Republic of)

2010-04-15

This study was designed to compare the ultrasonographic features of papillary thyroid carcinoma with and without Hashimoto's thyroiditis. This retrospective study included 190 patients with papillary thyroid carcinoma which was proven by neck surgery. The difference in the ultrasonographic findings between papillary thyroid carcinoma with Hashimoto's thyroiditis and papillary thyroid carcinoma without Hashimoto's thyroiditis were calculated statistically. Hashimoto's thyroiditis was diagnosed in 61 of 190 patients following neck surgery. The incidence of coexisting papillary thyroid carcinoma with Hashimoto's thyroiditis was significantly higher in women (p=0.0026). In addition, the frequency of macrocalcification in patients with Hashimoto's thyroiditis was also significantly higher (p=0.0009). Conversely,other ultrasonographic findings including the shape, margin, echogenicity and calcifications, for patients with papillary thyroid carcinoma with Hashimoto's thyroiditis and papillary thyroid carcinoma without Hashimoto's thyroiditis, were not statistically significant. We also found that patients with Hashimoto's thyroiditis who showed no calcification on ultrasonography tended not to detect the papillary carcinoma at a higher frequency. On ultrasonography, macrocalcifications occurred more frequently in patients with Hashimoto's thyroiditis than those without Hashimoto's thyroiditis. Malignant thyroid nodules without calcifications in patients with Hashimoto's thyroiditis more often could not be detected. Therefore, it is important carefully examine patients with Hashimoto's thyroiditis

Prenatal ultrasonographic findings of cloacal anomaly

Energy Technology Data Exchange (ETDEWEB)

Song, Mi Jin [Samsung Cheil Hospital, Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of)

2002-09-15

To evaluate the ultrasonographic characteristic of a rare malformation comples, Cloacal anomaly on prenatal ultrasonography. From March 1991 to July 2001, eight cases with the persistent cloaca (4 cases in female and 1 case in male) and cloacal exstrophy (3 cases) diagnosed by prenatal ultrasound examination were included, and all of them were pathologically confirmed by autopsy. One radiologist retrospectively analyzed the prenatal sonographic images, including the urinary bladder, kidney, pelvic cyst, abdominal wall defect and amount of amniotic fluid. The ultrasonographic diagnosis was established at 21.8 {+-} 7.8 weeks of gestation. The prenatal ultrasonographic findings of the persistent cloaca were absent bladder (n=2), distended bladder (n=2) and small thick bladder (n=1). Sonography of the kidney showed normal (n=2), hydronephrosis (n=1), dysplasia (n=1) and unilateral hydronephrosis with absent contralateral kidney (n=1). Four fetuses showed septated pelvic cyst; three fetuses, oligohydramnios. The prenatal ultrasonographic findings of cloacal exstrophy included absent bladder (n=3), normal kidney (n=1), hydronephrosis (n=1) and absent kidney (n=1). All fetuses with cloacal exstrophy had abdominal wall defect while two of them had oligohydramnios. A prenatal diagnosis of persistent cloaca can be confidently made when there is septated pelvic cyst combined oligohydramnios, sediments within the cyst and intraluminal calcifications. Cloacal exstrophy should be included in diagnosis if there is a low abdominal wall defect with absent urinary bladder.

Ultrasonographic ejection fraction of normal gallbladder

Energy Technology Data Exchange (ETDEWEB)

Park, Jin Hun; Kim, Seung Yup; Park, Yaung Hee; Kang, Ik Won; Yoon, Jong Sup [Hangang Sacred Heart Hospital, Halym College, Chuncheon (Korea, Republic of)

1984-06-15

Real-time ultrasonography is a simple, accurate, noninvasive and potentially valuable means of studying gallbladder size and emptying. The authors calculated ultrasonographically the ejection fraction of 80 cases of normally functioning gallbladder on oral cholecystography, from June 1983 to April 1984, at the department of radiology, Hangang Sacred Heart Hospital. The results were obtained as follows; 1. Ultrasonographic Ejection Fraction at 30 minutes after the fatty meal was 73.1{+-}16.85. 2. There was no significant difference in age and sex, statistically.

Ultrasonographic diagnosis of acute appendicitis

International Nuclear Information System (INIS)

Lee, Sang Hun; Chang, Young Duk; Kim, Dae Ho; Lee, Hae Kyung; Kwon, Kui Hyang; Kim, Ki Jung

1988-01-01

Acute appendicitis is the most common surgical disease of acute abdomen, But the diagnosis of acute appendicitis is often difficult, and not in frequently, operation for appendicitis is performed only to find a normal appendix. Various radiological examinations have been proposed to improve diagnostic accuracy of appendicitis. The purpose of this study was to improve the diagnostic accuracy of appendicitis, and to decline negative exploration. High resolution real time ultrasonographical examination using graded compression was performed in 57 consecutive patients who were clinically suspected of appendicitis. Autors analysed ultrasonographical, surgical, and clinical follow up findings. The results were are follows: 1. Ultrasonographical finding of acute appendicitis was visualization of appendix as a tubular structure with one bline end, or target phenomenon. 2. Hypoechoic area over the appendix was thought to be a sign of periappendiceal abscess. 3. The sensitivity of US diagnosis of acute appendicitis in this study was 92.8% with a specificity of 93.1%. The overall accuracy was 93.0%. 4. In control group of 50 individuals, the abnormal appendix was not visualized. 5. In cases of clinically suspected appendicitis, the US evaluation with graded compression technique is very accurate and effective examination.

Ultrasonographic diagnosis of stomach cancer

Energy Technology Data Exchange (ETDEWEB)

Chung, Eun Chul; Jin, S I; Kim, J W [Seoul National University College of Medicine, Seoul (Korea, Republic of)

1982-12-15

The ultrasonographic features of stomach cancer were studied in 43 patients who were diagnosed by double contrast UGI study and endoscopy. Ultrasonographic study was performed immediately after UGI study and the findings were correlated with USI study. The authors observed infiltrative lesions causing thickening of gastric wall, which can be localized (16 of 31) or diffuse(15 of 31). 9 cases were exogastric masses, sonographic findings were not found in the lesions occupying the cardia (3 of 5) Ultrasonography is useful in demonstrating the extent of the tumor and the presence of metastasis elsewhere in the abdomen, facilitating tumor staging and evaluation of the response to therapy

The reliability of computer analysis of ultrasonographic prostate images: the influence of inconsistent histopathology

NARCIS (Netherlands)

Giesen, R. J.; Huynen, A. L.; de la Rosette, J. J.; Schaafsma, H. E.; van Iersel, M. P.; Aarnink, R. G.; Debruyne, F. M.; Wijkstra, H.

1994-01-01

This article describes a method to investigate the influence of inconsistent histopathology during the development of tissue discrimination algorithms. Review of the pathology is performed on the biopsies used as training set of a computer system for cancer detection in ultrasonographic prostate

Prenatal ultrasonographic findings of cloacal anomaly

International Nuclear Information System (INIS)

Song, Mi Jin

2002-01-01

To evaluate the ultrasonographic characteristic of a rare malformation comples, Cloacal anomaly on prenatal ultrasonography. From March 1991 to July 2001, eight cases with the persistent cloaca (4 cases in female and 1 case in male) and cloacal exstrophy (3 cases) diagnosed by prenatal ultrasound examination were included, and all of them were pathologically confirmed by autopsy. One radiologist retrospectively analyzed the prenatal sonographic images, including the urinary bladder, kidney, pelvic cyst, abdominal wall defect and amount of amniotic fluid. The ultrasonographic diagnosis was established at 21.8 ± 7.8 weeks of gestation. The prenatal ultrasonographic findings of the persistent cloaca were absent bladder (n=2), distended bladder (n=2) and small thick bladder (n=1). Sonography of the kidney showed normal (n=2), hydronephrosis (n=1), dysplasia (n=1) and unilateral hydronephrosis with absent contralateral kidney (n=1). Four fetuses showed septated pelvic cyst; three fetuses, oligohydramnios. The prenatal ultrasonographic findings of cloacal exstrophy included absent bladder (n=3), normal kidney (n=1), hydronephrosis (n=1) and absent kidney (n=1). All fetuses with cloacal exstrophy had abdominal wall defect while two of them had oligohydramnios. A prenatal diagnosis of persistent cloaca can be confidently made when there is septated pelvic cyst combined oligohydramnios, sediments within the cyst and intraluminal calcifications. Cloacal exstrophy should be included in diagnosis if there is a low abdominal wall defect with absent urinary bladder.

Ultrasonographic findings of breast diseases during pregnancy and lactating period

Energy Technology Data Exchange (ETDEWEB)

Lee, Yeon Hee [Dnakook University College of Medicine, Cheonan (Korea, Republic of); Park, Yong Hyun; Kwon, Tae Hee [Cha Women' s Hospital of Seoul, Seoul (Korea, Republic of)

1995-09-15

To evaluate ultrasonographic findings and usefulness in the diagnosis of breast diseases during pregnancy and lactating period. The authors evaluated the ultrasonographic findings of 18 breast diseases during pregnancy and lactation retrospectively. The ultrasonographic examinations were performed with linear-array 5 MHz transducer (ATL). Final diagnoses were obtained by the excisional biopsy, fine needle aspiration and clinical follow-up. Total 18 cases of breast diseases were consisted of 8 cases of galactocele, 4 cases of fibroadenoma, 3 cases of axillary accessory breast, 2 cases of lactating adenoma, and 1 case of phylloides tumor. The ultrasonographic findings of the above breast diseases were valuable in the diagnosis and therapeutic planning. Ultrasonography is the initial and useful method of diagnosing breast diseases during pregnancy and lactating period.

Ultrasonographic findings of breast diseases during pregnancy and lactating period

International Nuclear Information System (INIS)

Lee, Yeon Hee; Park, Yong Hyun; Kwon, Tae Hee

1995-01-01

To evaluate ultrasonographic findings and usefulness in the diagnosis of breast diseases during pregnancy and lactating period. The authors evaluated the ultrasonographic findings of 18 breast diseases during pregnancy and lactation retrospectively. The ultrasonographic examinations were performed with linear-array 5 MHz transducer (ATL). Final diagnoses were obtained by the excisional biopsy, fine needle aspiration and clinical follow-up. Total 18 cases of breast diseases were consisted of 8 cases of galactocele, 4 cases of fibroadenoma, 3 cases of axillary accessory breast, 2 cases of lactating adenoma, and 1 case of phylloides tumor. The ultrasonographic findings of the above breast diseases were valuable in the diagnosis and therapeutic planning. Ultrasonography is the initial and useful method of diagnosing breast diseases during pregnancy and lactating period

Airplane detection in remote sensing images using convolutional neural networks

Science.gov (United States)

Ouyang, Chao; Chen, Zhong; Zhang, Feng; Zhang, Yifei

2018-03-01

Airplane detection in remote sensing images remains a challenging problem and has also been taking a great interest to researchers. In this paper we propose an effective method to detect airplanes in remote sensing images using convolutional neural networks. Deep learning methods show greater advantages than the traditional methods with the rise of deep neural networks in target detection, and we give an explanation why this happens. To improve the performance on detection of airplane, we combine a region proposal algorithm with convolutional neural networks. And in the training phase, we divide the background into multi classes rather than one class, which can reduce false alarms. Our experimental results show that the proposed method is effective and robust in detecting airplane.

Arm and neck pain in ultrasonographers.

Science.gov (United States)

Claes, Frank; Berger, Jan; Stassijns, Gaëtane

2015-03-01

The aim of this study was to evaluate the prevalence of upper-body-quadrant pain among ultrasonographers and to evaluate the association between individual ergonomics, musculoskeletal disorders, and occurrence of neck pain. A hundred and ten (N = 110) Belgian and Dutch male and female hospital ultrasonographers were consecutively enrolled in the study. Data on work-related ergonomic and musculoskeletal disorders were collected with an electronic inquiry, including questions regarding ergonomics (position of the screen, high-low table, and ergonomic chair), symptoms (neck pain, upper-limb pain), and work-related factors (consecutive working hours a day, average working hours a week). Subjects with the screen on their left had significantly more neck pain (odds ratio [OR] = 3.6, p = .0286). Depending on the workspace, high-low tables increased the chance of developing neck pain (OR = 12.9, p = .0246). A screen at eye level caused less neck pain (OR = .22, p = .0610). Employees with a fixed working space were less susceptible to arm pain (OR = 0.13, p = .0058). The prevalence of arm pain was significantly higher for the vascular department compared to radiology, urology, and gynecology departments (OR = 9.2, p = .0278). Regarding prevention of upper-limb pain in ultrasonograph, more attention should be paid to the work environment and more specialty to the ultrasound workstation layout. Primary ergonomic prevention could provide a painless work situation for the ultrasonographer. Further research on the ergonomic conditions of ultrasonography is necessary to develop ergonomic solutions in the work environment that will help to alleviate neck and arm pain. © 2014, Human Factors and Ergonomics Society.

Ultrasonographic findings of lateral epicondylitis of humerus

Energy Technology Data Exchange (ETDEWEB)

Choi, Joon Hyuk; Ha, Doo Heo [Pundang CHA Univ., Seongnam (Korea, Republic of)

2002-03-01

To evaluate the ultrasonographic findings of lateral epicondylitis and their relationship with clinical outcome. The findings of ultrasonographic examinations of eighteen elbow joints in 15 patients (M:F=5:10; age:38-65(mean, 47.6) years) with lateral epicondylitis were reviewed. Two patients underwent surgery, two were not treated, and the remaining 11 were treated conservatively. Symptomatic improvement was noted 1 week after conservative treatment in two cases, at 2 weeks in five cases, at 3 weeks in three cases, and at 5 weeks in one case. With patients in the 90 degree flexed elbow position and in a supinated wrist, weexamined the extensor carpi radialis brevis (ECRB) tendon around the lateral epicondyle using ultrasound equipment with a 7-11 MHz linear transducer. The findings were assessed in terms of swelling of the tendon, changes in its echotexture, the presence of calcification of cystic degeneration, loss of the hypoechoic band between the tendon and bony cortex of the lateral epicondyle, cortical irregularity of the lateral epicondyle, and fluid collection around the tendon. Any relationships between each ultrasonographic finding and the treatment interval after which symptomatic improvement was noted were evaluated. In the 18 joints, change was heterogeneous hypoechogenicity in 13, and heterogeneous mixed echogenicity in three. Other ultrasonographic findings were swelling of the tendon in ten cases, loss of the hypoechoic band in 14, cortical irregularity in five, calcification in four, cystic degeneration in nine, and fluid collection around the tendon in four. In patients treated conservatively, there was no statistically significant difference between each ultrasonographic finding and the treatment interval after which symptomatic improvement was noted. Ultrasonography can be used to assess changes in the ECRB tendon and lateral epicondyle occurring in lateral epicondylitis, but fails to provide information on the rapidity of symptomatic

Ultrasonographic findings of lateral epicondylitis of humerus

International Nuclear Information System (INIS)

Choi, Joon Hyuk; Ha, Doo Heo

2002-01-01

To evaluate the ultrasonographic findings of lateral epicondylitis and their relationship with clinical outcome. The findings of ultrasonographic examinations of eighteen elbow joints in 15 patients (M:F=5:10; age:38-65(mean, 47.6) years) with lateral epicondylitis were reviewed. Two patients underwent surgery, two were not treated, and the remaining 11 were treated conservatively. Symptomatic improvement was noted 1 week after conservative treatment in two cases, at 2 weeks in five cases, at 3 weeks in three cases, and at 5 weeks in one case. With patients in the 90 degree flexed elbow position and in a supinated wrist, weexamined the extensor carpi radialis brevis (ECRB) tendon around the lateral epicondyle using ultrasound equipment with a 7-11 MHz linear transducer. The findings were assessed in terms of swelling of the tendon, changes in its echotexture, the presence of calcification of cystic degeneration, loss of the hypoechoic band between the tendon and bony cortex of the lateral epicondyle, cortical irregularity of the lateral epicondyle, and fluid collection around the tendon. Any relationships between each ultrasonographic finding and the treatment interval after which symptomatic improvement was noted were evaluated. In the 18 joints, change was heterogeneous hypoechogenicity in 13, and heterogeneous mixed echogenicity in three. Other ultrasonographic findings were swelling of the tendon in ten cases, loss of the hypoechoic band in 14, cortical irregularity in five, calcification in four, cystic degeneration in nine, and fluid collection around the tendon in four. In patients treated conservatively, there was no statistically significant difference between each ultrasonographic finding and the treatment interval after which symptomatic improvement was noted. Ultrasonography can be used to assess changes in the ECRB tendon and lateral epicondyle occurring in lateral epicondylitis, but fails to provide information on the rapidity of symptomatic

Reliability of ultrasonographic measurements in suspected patients of developmental dysplasia of the hip and correlation with the acetabular index

Directory of Open Access Journals (Sweden)

Cem Copuroglu

2011-01-01

Full Text Available Background: Ultrasonography is accepted as a useful imaging modality in the early detection of developmental dysplasia of the hip (DDH. Early detection and early treatment of DDH prevents hip dislocation and related physical, social, economic, and psychological problems. The purpose of this study was to evaluate the reliability of ultrasonographic and roentgenographic measurements measured by seven different observers. Materials and Methods: The alpha angles of 66 hips in 33 patients were measured using the Graf method by seven different observers. Acetabular index degrees on plane roentgenograms were measured in order to assess the correlation between the ultrasonographic alpha angle and the radiographic acetabular index, which both show the bony acetabular depth, retrospectively. Results: The interclass correlation coefficient, measuring the interobserver reliability, was high and statistically significant for the ultrasonographic measurements. There was a negative correlation between the alpha angle and the acetabular index. Conclusions: Ultrasonography, when applied properly, is a reliable technique between different observers, in the diagnosis and follow up of DDH. When assessed concomitantly with the roentgenographic measurements, the results are reliable and statistically meaningful.

A case of alkaptonuria - ultrasonographic findings.

Science.gov (United States)

Damian, Laura Otilia; Felea, Ioana; Boloşiu, Călin; Botar-Jid, Carolina; Fodor, Daniela; Rednic, Simona

2013-12-01

Alkaptonuria is a rare disease with autosomal recessive inheritance and variable expression. The weight-bearing joint involvement and spondylitis-like vertebral changes occur only after the 3rd decade. Musculoskeletal ultrasonographic findings in alkaptonuria were only rarely described, consisting mainly into enthesopathy and non-synovial tendon degeneration. We present the case of a 50 years old man with alkaptonuria and discuss the ultrasonographic findings and the relationship of the disease with chondrocalcinosis. The tendinous and synovial aspect may be peculiar and it could therefore allow recognition and screening for alkaptonuria, along with clinical and radiologic data.

Radial basis function neural network in fault detection of automotive ...

African Journals Online (AJOL)

Radial basis function neural network in fault detection of automotive engines. ... Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults ... Keywords: Automotive engine, independent RBFNN model, RBF neural network, fault detection

Ultrasonographic diagnosis of ureteral stones: Accuracy and factors influencing on diagnostic sensitivity

Energy Technology Data Exchange (ETDEWEB)

Park, Young Mi; Han, Sang Seok; Chang, Seung Kuk; Joo, Sang Hoo; Lee, Jeong Sik; Eun, Choong Ki [Pusan Paik Hospital, Inje University College of Medicine, Pusan (Korea, Republic of)

1999-12-15

To determine the accuracy of ultrasonographic diagnosis in patients with clinically suspected ureteral stones and to evaluate the factors influencing on the diagnostic sensitivity for the detection of ureteral stone. The patients (115 cases) with proven presence or absence of ureteral stones were included in the study. At first, both sided kidney and proximal ureters were examined on each decubitus position and then middle ureters were done if proximal ureters were visualized. On the supine view, distal ureters and UVJ were scanned through the acoustic window of the filled bladder. KUB (20 cases), IVU (62 cases), AGP (7 cases), RGP (3 cases), ESWL (9 cases), CT (9 cases), and patients' history of spontaneous passage of stones (5 cases) were included as confirmation methods. The sensitivity, specificity, and accuracy of the ultrasonographic diagnosis of ureteral stones were calculated and the factors influencing on the sensitivity on the focus of the position and size of ureteral stone, visibility of ureter, the presence or absence of renal stone and hydronephrosis were analyzed. Of 82 cases with proven ureteral stone, 72 cases were revealed on ultrasonography and there was one false positive examination among 33 cases with proven absence of ureteral stone. The overall diagnostic accuracy was 90%. The ultrasonographic detection rates of ureteral stones as correlated with their locations were 83% (24/29), 100% (11/11), 80% (16/20), and 100% (21/21) of each group of proximal, middle, distal ureter, and UVJ stones. Of 61 stones, those as correlated with their sizes, were 82% (37/45) and 94% (15/16) of each group less than 10 mm and more than 11 mm. Those as correlated with the presence or absence of ureteral visualization on ultrasonography were 92% (69/75) and 43% (3/7) of each group. Those as correlated with presence of absence of renal stones were 85% (41/48) and 91% (31/34) of each group. Those as correlated with presence or absence of hydronephrosis were 89

A Decline in Response Variability Improves Neural Signal Detection during Auditory Task Performance.

Science.gov (United States)

von Trapp, Gardiner; Buran, Bradley N; Sen, Kamal; Semple, Malcolm N; Sanes, Dan H

2016-10-26

The detection of a sensory stimulus arises from a significant change in neural activity, but a sensory neuron's response is rarely identical to successive presentations of the same stimulus. Large trial-to-trial variability would limit the central nervous system's ability to reliably detect a stimulus, presumably affecting perceptual performance. However, if response variability were to decrease while firing rate remained constant, then neural sensitivity could improve. Here, we asked whether engagement in an auditory detection task can modulate response variability, thereby increasing neural sensitivity. We recorded telemetrically from the core auditory cortex of gerbils, both while they engaged in an amplitude-modulation detection task and while they sat quietly listening to the identical stimuli. Using a signal detection theory framework, we found that neural sensitivity was improved during task performance, and this improvement was closely associated with a decrease in response variability. Moreover, units with the greatest change in response variability had absolute neural thresholds most closely aligned with simultaneously measured perceptual thresholds. Our findings suggest that the limitations imposed by response variability diminish during task performance, thereby improving the sensitivity of neural encoding and potentially leading to better perceptual sensitivity. The detection of a sensory stimulus arises from a significant change in neural activity. However, trial-to-trial variability of the neural response may limit perceptual performance. If the neural response to a stimulus is quite variable, then the response on a given trial could be confused with the pattern of neural activity generated when the stimulus is absent. Therefore, a neural mechanism that served to reduce response variability would allow for better stimulus detection. By recording from the cortex of freely moving animals engaged in an auditory detection task, we found that variability

Abdominal endometriosis: Ultrasonographic findings (report of two cases)

Energy Technology Data Exchange (ETDEWEB)

Lee, Jong Beum; Kim, Yong Goo; Lee, Yong Chul; Kim, Kun Sang [Chung Ang University Hospital, Seoul (Korea, Republic of)

1993-12-15

Endometriosis in the abdominal wall is a rare condition that most commonly occurs in the physiological scar of the umbilicus and in surgical scars of pelvic operation. The ultrasonographic findings are often non-specific, but with scrutinized physical examination and history, correct diagnosis can be made. We report ultrasonographic findings of abdominal wall endometriosis in two cases, both of which were related to previous cesarian section scar

Epileptiform spike detection via convolutional neural networks

DEFF Research Database (Denmark)

Johansen, Alexander Rosenberg; Jin, Jing; Maszczyk, Tomasz

2016-01-01

The EEG of epileptic patients often contains sharp waveforms called "spikes", occurring between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we develop a convolutional neural network (CNN) for detecting spikes in EEG of epileptic patients in an automated...

Anomaly detection in an automated safeguards system using neural networks

International Nuclear Information System (INIS)

Whiteson, R.; Howell, J.A.

1992-01-01

An automated safeguards system must be able to detect an anomalous event, identify the nature of the event, and recommend a corrective action. Neural networks represent a new way of thinking about basic computational mechanisms for intelligent information processing. In this paper, we discuss the issues involved in applying a neural network model to the first step of this process: anomaly detection in materials accounting systems. We extend our previous model to a 3-tank problem and compare different neural network architectures and algorithms. We evaluate the computational difficulties in training neural networks and explore how certain design principles affect the problems. The issues involved in building a neural network architecture include how the information flows, how the network is trained, how the neurons in a network are connected, how the neurons process information, and how the connections between neurons are modified. Our approach is based on the demonstrated ability of neural networks to model complex, nonlinear, real-time processes. By modeling the normal behavior of the processes, we can predict how a system should be behaving and, therefore, detect when an abnormality occurs

Detecting atrial fibrillation by deep convolutional neural networks.

Science.gov (United States)

Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui

2018-02-01

Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning. The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms. Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved. The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

A neural network approach to burst detection.

Science.gov (United States)

Mounce, S R; Day, A J; Wood, A S; Khan, A; Widdop, P D; Machell, J

2002-01-01

This paper describes how hydraulic and water quality data from a distribution network may be used to provide a more efficient leakage management capability for the water industry. The research presented concerns the application of artificial neural networks to the issue of detection and location of leakage in treated water distribution systems. An architecture for an Artificial Neural Network (ANN) based system is outlined. The neural network uses time series data produced by sensors to directly construct an empirical model for predication and classification of leaks. Results are presented using data from an experimental site in Yorkshire Water's Keighley distribution system.

Artificial neural network detects human uncertainty

Science.gov (United States)

Hramov, Alexander E.; Frolov, Nikita S.; Maksimenko, Vladimir A.; Makarov, Vladimir V.; Koronovskii, Alexey A.; Garcia-Prieto, Juan; Antón-Toro, Luis Fernando; Maestú, Fernando; Pisarchik, Alexander N.

2018-03-01

Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allow the recognition of specific forms of brain activity from multichannel EEG or MEG data. This makes the ANN an efficient computational core for brain-machine systems. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns of neural activity, the use of ANNs for recognition and classification of patterns in neural networks still requires additional attention, especially in ambiguous situations. According to this, in this research, we demonstrate the efficiency of application of the ANN for classification of human MEG trials corresponding to the perception of bistable visual stimuli with different degrees of ambiguity. We show that along with classification of brain states associated with multistable image interpretations, in the case of significant ambiguity, the ANN can detect an uncertain state when the observer doubts about the image interpretation. With the obtained results, we describe the possible application of ANNs for detection of bistable brain activity associated with difficulties in the decision-making process.

Time of initial detection of fetal and extra-fetal structures by ultrasonographic examination in Miniature Schnauzer bitches.

Science.gov (United States)

Kim, Bang Sil; Son, Chang Ho

2007-09-01

Serial ultrasonographic examinations were performed daily on 9 Miniature Schnauzer bitches from the 15th day of gestation until parturition to determine the time the gestational structures were first detected. The gestational age was timed from the day of ovulation (day 0), which was estimated to occur when the plasma progesterone concentration was >4.0 ng/ml. The gestational length in 9 Miniature Schnauzer bitches was found to be 63.0 +/- 1.7 (range 61-65) days. The initial detection of the fetal and extra-fetal structures were as follows: gestational sac at day 18.0 +/- 0.9 (17-19); zonary placenta in the uterine wall at day 24.9 +/- 1.1 (23-26); yolk sac membrane at day 25.0 +/- 0.9 (24-26); amnionic membrane at day 27.7 +/- 1.0 (26- 29); embryo initial detection at day 22.6 +/- 0.5 (22-23); heartbeat at day 23.4 +/- 0.5 (23-24); fetal movement at day 32.5 +/- 0.8 (32-34); stomach at day 31.2 +/- 1.6 (29-33); urinary bladder at day 32.6 +/- 1.8 (31-35); skeleton at day 34.9 +/- 1.6 (34-38) and kidney at day 42.2 +/- 0.7 (41-43).

External validation of fatty liver index for identifying ultrasonographic fatty liver in a large-scale cross-sectional study in Taiwan.

Directory of Open Access Journals (Sweden)

Bi-Ling Yang

Full Text Available The fatty liver index (FLI is an algorithm involving the waist circumference, body mass index, and serum levels of triglyceride and gamma-glutamyl transferase to identify fatty liver. Although some studies have attempted to validate the FLI, few studies have been conducted for external validation among Asians. We attempted to validate FLI to predict ultrasonographic fatty liver in Taiwanese subjects.We enrolled consecutive subjects who received health check-up services at the Taipei Veterans General Hospital from 2002 to 2009. Ultrasonography was applied to diagnose fatty liver. The ability of the FLI to detect ultrasonographic fatty liver was assessed by analyzing the area under the receiver operating characteristic (AUROC curve.Among the 29,797 subjects enrolled in this study, fatty liver was diagnosed in 44.5% of the population. Subjects with ultrasonographic fatty liver had a significantly higher FLI than those without fatty liver by multivariate analysis (odds ratio 1.045; 95% confidence interval, CI 1.044-1.047, p< 0.001. Moreover, FLI had the best discriminative ability to identify patients with ultrasonographic fatty liver (AUROC: 0.827, 95% confidence interval, 0.822-0.831. An FLI < 25 (negative likelihood ratio (LR- 0.32 for males and <10 (LR- 0.26 for females rule out ultrasonographic fatty liver. Moreover, an FLI ≥ 35 (positive likelihood ratio (LR+ 3.12 for males and ≥ 20 (LR+ 4.43 for females rule in ultrasonographic fatty liver.FLI could accurately identify ultrasonographic fatty liver in a large-scale population in Taiwan but with lower cut-off value than the Western population. Meanwhile the cut-off value was lower in females than in males.

Ultrasonographic features of normal lower ureters

International Nuclear Information System (INIS)

Kim, Young Soon; Bae, M. Y.; Park, K. J.; Jeon, H. S.; Lee, J. H.

1990-01-01

Although ultrasonographic evaluation of the normal ureters is difficult due to bowel gas, the lower segment of the normal ureters can be visualized using the urinary bladder as an acoustic window. Authors prospetively performed ultrasonography with the standard suprapubic technique and analyzed the ultrasonographic features of normal lower ureters in 79 cases(77%). Length of visualized segment of the distal ureter ranged frp, 1.5cm to 7.2 cm and the visualized segment did not exceed 3.9mm in maximum diameter. Knowledge of sonographic features of the normal lower ureters can be helpful in the evaluation of pathologic or suspected pathologic conditions of the lower ureters

Ultrasonographic Observations of the Pleural Effusion

International Nuclear Information System (INIS)

Lee, Dong Hoo; Park, Sung Soo; Lee, Chung Hee

1982-01-01

Five cases of patients with pleural effusion were evaluated by the grey-scale ultrasonography. Ultrasonography of pleural effusion in each case was represented as fluid accumulation within the pleural cavity with anechoic crescent moon shape or saddle appearance marginated by diaphragm. Ptosis of the liver with demonstrable right diaphragm was assessment in the severe right pleural effusion. it is emphasized that the practical advantages of the ultrasonographic approach were notable both in establishing diagnosis and in treatment of pleural effusion,with special regarding of noninvasiveness particularly in the women of pregnancy, of staging in the patient with malignant lymphoma, and of safety in a subsequent thoracentesis under the ultrasonographic guidance

Ultrasonographic Observations of the Pleural Effusion

Energy Technology Data Exchange (ETDEWEB)

Lee, Dong Hoo; Park, Sung Soo; Lee, Chung Hee [Hanyang University School of Medicine, Seoul (Korea, Republic of)

1982-12-15

Five cases of patients with pleural effusion were evaluated by the grey-scale ultrasonography. Ultrasonography of pleural effusion in each case was represented as fluid accumulation within the pleural cavity with anechoic crescent moon shape or saddle appearance marginated by diaphragm. Ptosis of the liver with demonstrable right diaphragm was assessment in the severe right pleural effusion. it is emphasized that the practical advantages of the ultrasonographic approach were notable both in establishing diagnosis and in treatment of pleural effusion,with special regarding of noninvasiveness particularly in the women of pregnancy, of staging in the patient with malignant lymphoma, and of safety in a subsequent thoracentesis under the ultrasonographic guidance

Ultrasonographic evaluation of the shoulder in elite Italian beach volleyball players.

Science.gov (United States)

Monteleone, G; Tramontana, A; Mc Donald, K; Sorge, R; Tiloca, A; Foti, C

2015-10-01

Beach volleyball is an overhead sport that subjects the hitting shoulder to intense functional loads. The purpose of this study is to identify ultrasonographically the prevalence of myotendinous alterations in professional Italian beach volleyball players at the Italian championship and to look for associations between ultrasound findings and the other data collected. Fifty-three beach volleyball players (31 women, 22 men) were recruited during the second stage of the Italian championship held in July 2012 in Rome, Italy. Clinical history was obtained from all subjects, followed by physical exam. Each athlete completed a questionnaire regarding sports activities. Bilateral ultrasonographic evaluation of the shoulders was then performed. Calcific tendinopathy of the rotator cuff of the hitting shoulder was identified ultrasonographically in 30% of the athletes. The mean age of the athletes with calcific tendinopathy was older than subjects with other abnormalities on ultrasonographic examination (33.1 years vs. 25.8 years, t-test; Pvolleyball players has a prevalence of 30% ultrasonographically, greater than that reported in the general population. In these athletes, the presence of calcific tendinopathy correlates positively with age.

Ultrasonographic Features of Papillary Thyroid Carcinomas According to Their Subtypes

Directory of Open Access Journals (Sweden)

Hye Jin Baek

2018-05-01

Full Text Available BackgroundThe ultrasonographic characteristics and difference for various subtypes of papillary thyroid carcinoma (PTC are still unclear. The aim of this study was to compare the ultrasonographic features of PTC according to its subtype in patients undergoing thyroid surgery.MethodsIn total, 140 patients who underwent preoperative thyroid ultrasonography (US and thyroid surgery between January 2016 and December 2016 were included. The ultrasonographic features and the Korean Thyroid Imaging Reporting and Data System (K-TIRADS category of each thyroid nodule were retrospectively evaluated by a single radiologist, and differences in ultrasonographic features according to the PTC subtype were assessed.ResultsAccording to histopathological analyses, there were 97 classic PTCs (62.2%, 34 follicular variants (21.8%, 5 tall cell variants (3.2%, 2 oncocytic variants (1.3%, 1 Warthin-like variant (0.6%, and 1 diffuse sclerosing variant (0.6%. Most PTCs were classified under K-TIRADS category 5. Among the ultrasonographic features, the nodule margin and the presence of calcification were significantly different among the PTC subtypes. A spiculated/microlobulated margin was the most common type of margin, regardless of the PTC subtype. In particular, all tall cell variants exhibited a spiculated/microlobulated margin. The classic PTC group exhibited the highest prevalence of intranodular calcification, with microcalcification being the most common. The prevalence of multiplicity and nodal metastasis was high in the tall cell variant group.ConclusionThe majority of PTCs in the present study belonged to K-TIRADS category 5, regardless of the subtype. Our findings suggest that ultrasonographic features are not useful for distinguishing PTC subtypes.

Ultrasonographic assessment of the male koala (Phascolarctos cinereus) reproductive tract.

Science.gov (United States)

Larkin, Rebecca; Palmieri, Chiara; Oishi, Motoharu; Hulse, Lyndal; Johnston, Stephen D

2018-04-01

Studies documenting the application of ultrasonography to depict normal and pathological changes in koalas (Phascolarctos cinereus), especially in the male, are scarce. Sixty-two wild koalas were used in this study to define ultrasonographic protocols and features for the assessment of the male koala reproductive tract. Testis, epididymis and spermatic cord were examined using a hockey stick transducer. The normal koala testis showed a homogeneous echogenicity and an obvious hyper-echoic band corresponding to the tunica albuginea. The cauda epididymis was characterised by hypo- and hyper-echoic regions and was most effectively imaged in sagittal section. The koala prostate was assessed using a micro-curved transducer positioned midline, caudal to the bladder. On transverse section, it showed distinct margins and a well-defined internal structure, although the prostatic urethra was not apparent on most scans. To image the bulbourethral glands (BGs), the hockey stick transducer was placed lateral to the cloaca. BGIII was located just below the skin, while BGII was located deeper than BGIII. BGI was too small and not sufficiently echogenic to be detected. The ultrasonographic appearance of the BGs was similar to that of the testes but with more obvious hypo-echoic stippling. This comprehensive review of the ultrasonographic appearance of normal male koala reproductive tract can be used by veterinarians and others, in zoos or those working with wild koalas, during assessment of the reproductive tract of male koalas in relation to seasonal changes in accessory gland function or for the pathological investigation of reproductive lesions and infertility problems. Copyright © 2017 Elsevier Ltd. All rights reserved.

Ultrasonographic findings of Myoma, H-mole and Missed abortion

International Nuclear Information System (INIS)

Huh, Nam Yoon; You, H. S.; Seong, K. J.; Park, C. Y.

1982-01-01

Ultrasonography is very important in the diagnosis of various kinds of diseases in Obsterics and Gynecology. It has high diagnostic accuracy in the diagnosis of pelvic masses and widely used for the detection of normal orpathologic pregnancy. But still it is difficult to differentiate degenerated myoma, H-mole and missed abortion by ultrasonography. So the authors analyzed the ultrasonographic findings of 81 patients with myoma(29 cases), H-mole(23 cases), and missed abortion(29 cases) and the results are as follows; 1. Diagnostic accuracy was 8.6% in myoma, 87% in H-mole and 89% in missed abortion. 2. The most typical ultrasonographic finding of myoma was obulated mass contour with nonhomogenous internal echo. 3. The most characteristic finding of H-mole was fine vesicular pattern internal echo with globular enlargement of uterus. 4. The most frequent finding of missed abortion was deformed gestational sac with or without remained fetal echo. 5. Clinical correlation was very important for accurate diagnosis, especially when differential diagnosis was very difficult between myoma with marked cystic degeneration, missed abortion with large distorted gestational sac and H-mole with severe degeneration

Computational neural network regression model for Host based Intrusion Detection System

Directory of Open Access Journals (Sweden)

Sunil Kumar Gautam

2016-09-01

Full Text Available The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN model and Multilayer Perceptron Neural Network (MPNN model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM significantly improved the detection accuracy while retaining minimum false alarm rate.

PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET

Directory of Open Access Journals (Sweden)

S. Devaraju

2014-04-01

Full Text Available Intrusion Detection Systems are challenging task for finding the user as normal user or attack user in any organizational information systems or IT Industry. The Intrusion Detection System is an effective method to deal with the kinds of problem in networks. Different classifiers are used to detect the different kinds of attacks in networks. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the four types of classifiers used are Feed Forward Neural Network (FFNN, Generalized Regression Neural Network (GRNN, Probabilistic Neural Network (PNN and Radial Basis Neural Network (RBNN. The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency and False Alarm Rate is measured. It is proved that the reduced dataset is performing better than the full featured dataset.

Ultrasonographic visualization of bleeding sites can help control postpartum hemorrhage using intrauterine balloon tamponade.

Science.gov (United States)

Kondoh, Eiji; Konishi, Mitsunaga; Kariya, Yoshitaka; Konishi, Ikuo

2015-01-01

Identification of precise bleeding sites is generally important to control hemorrhage. Nevertheless, the optimal technique to detect the bleeding sites has not yet been fully defined for patients with life-threatening post partum hemorrhage. We describe that ultrasonographic visualization of bleeding sites can help control post partum hemorrhage using intrauterine balloon tamponade. © 2014 Wiley Periodicals, Inc.

VoIP attacks detection engine based on neural network

Science.gov (United States)

Safarik, Jakub; Slachta, Jiri

2015-05-01

The security is crucial for any system nowadays, especially communications. One of the most successful protocols in the field of communication over IP networks is Session Initiation Protocol. It is an open-source project used by different kinds of applications, both open-source and proprietary. High penetration and text-based principle made SIP number one target in IP telephony infrastructure, so security of SIP server is essential. To keep up with hackers and to detect potential malicious attacks, security administrator needs to monitor and evaluate SIP traffic in the network. But monitoring and following evaluation could easily overwhelm the security administrator in networks, typically in networks with a number of SIP servers, users and logically or geographically separated networks. The proposed solution lies in automatic attack detection systems. The article covers detection of VoIP attacks through a distributed network of nodes. Then the gathered data analyze aggregation server with artificial neural network. Artificial neural network means multilayer perceptron network trained with a set of collected attacks. Attack data could also be preprocessed and verified with a self-organizing map. The source data is detected by distributed network of detection nodes. Each node contains a honeypot application and traffic monitoring mechanism. Aggregation of data from each node creates an input for neural networks. The automatic classification on a centralized server with low false positive detection reduce the cost of attack detection resources. The detection system uses modular design for easy deployment in final infrastructure. The centralized server collects and process detected traffic. It also maintains all detection nodes.

An artifical neural network for detection of simulated dental caries

Energy Technology Data Exchange (ETDEWEB)

Kositbowornchai, S. [Khon Kaen Univ. (Thailand). Dept. of Oral Diagnosis; Siriteptawee, S.; Plermkamon, S.; Bureerat, S. [Khon Kaen Univ. (Thailand). Dept. of Mechanical Engineering; Chetchotsak, D. [Khon Kaen Univ. (Thailand). Dept. of Industrial Engineering

2006-08-15

Objects: A neural network was developed to diagnose artificial dental caries using images from a charged-coupled device (CCD)camera and intra-oral digital radiography. The diagnostic performance of this neural network was evaluated against a gold standard. Materials and methods: The neural network design was the Learning Vector Quantization (LVQ) used to classify a tooth surface as sound or as having dental caries. The depth of the dental caries was indicated on a graphic user interface (GUI) screen developed by Matlab programming. Forty-nine images of both sound and simulated dental caries, derived from a CCD camera and by digital radiography, were used to 'train' an artificial neural network. After the 'training' process, a separate test-set comprising 322 unseen images was evaluated. Tooth sections and microscopic examinations were used to confirm the actual dental caries status.The performance of neural network was evaluated using diagnostic test. Results: The sensitivity (95%CI)/specificity (95%CI) of dental caries detection by the CCD camera and digital radiography were 0.77(0.68-0.85)/0.85(0.75-0.92) and 0.81(0.72-0.88)/0.93(0.84-0.97), respectively. The accuracy of caries depth-detection by the CCD camera and digital radiography was 58 and 40%, respectively. Conclusions: The model neural network used in this study could be a prototype for caries detection but should be improved for classifying caries depth. Our study suggests an artificial neural network can be trained to make the correct interpretations of dental caries. (orig.)

An artifical neural network for detection of simulated dental caries

International Nuclear Information System (INIS)

Kositbowornchai, S.; Siriteptawee, S.; Plermkamon, S.; Bureerat, S.; Chetchotsak, D.

2006-01-01

Objects: A neural network was developed to diagnose artificial dental caries using images from a charged-coupled device (CCD)camera and intra-oral digital radiography. The diagnostic performance of this neural network was evaluated against a gold standard. Materials and methods: The neural network design was the Learning Vector Quantization (LVQ) used to classify a tooth surface as sound or as having dental caries. The depth of the dental caries was indicated on a graphic user interface (GUI) screen developed by Matlab programming. Forty-nine images of both sound and simulated dental caries, derived from a CCD camera and by digital radiography, were used to 'train' an artificial neural network. After the 'training' process, a separate test-set comprising 322 unseen images was evaluated. Tooth sections and microscopic examinations were used to confirm the actual dental caries status.The performance of neural network was evaluated using diagnostic test. Results: The sensitivity (95%CI)/specificity (95%CI) of dental caries detection by the CCD camera and digital radiography were 0.77(0.68-0.85)/0.85(0.75-0.92) and 0.81(0.72-0.88)/0.93(0.84-0.97), respectively. The accuracy of caries depth-detection by the CCD camera and digital radiography was 58 and 40%, respectively. Conclusions: The model neural network used in this study could be a prototype for caries detection but should be improved for classifying caries depth. Our study suggests an artificial neural network can be trained to make the correct interpretations of dental caries. (orig.)

Assessment of the age for a preventive ultrasonographic examination of the prostate in the dog.

Science.gov (United States)

Mantziaras, G; Alonge, S; Faustini, M; Luvoni, G C

2017-09-15

The prostate commonly develops benign prostatic hyperplasia (BPH) in dogs over 5 years, while in aged dogs other pathological findings might be revealed by ultrasonographic exam. The aim of the present study was to estimate the most suitable age for a preventive ultrasonographic examination of the prostate in the dog. The prostate of 1003 intact male dogs of 64 different breeds, of different ages (1-18 years) and bodyweights (2-55 kg) was evaluated with ultrasound, irrespective of the reason for clinical examination. The age of each dog was expressed as the ratio between the actual age and the maximum longevity expected for the breed. Dogs were divided in two groups based on breeds' life expectancy as short life (SL) and long life (LL). The size of the prostate (normal, enlarged or small) and the presence of abnormal sonographic findings were recorded for each dog. The results of the present study indicate that the most suitable age for a preventive ultrasonographic exam of the prostate in the dog is approximately at 40% of its expected longevity, both in short and long life breeds, because at this age there is a strong possibility to be able to detect abnormal prostatic findings. In 47.5% of the dogs at least one abnormal finding of the prostate was revealed by ultrasonographic exam, while dogs with long life expectancy showed a significantly higher prevalence of abnormalities, than dogs with short life expectancy. The most frequent findings were the increase of prostatic size (33.5%) and the presence of at least one cyst (33.6%), with no difference between SL and LL dogs. In conclusion, a preventive examination of the prostate starting at 40% of expected longevity in dogs of short and long life breeds is strongly recommended for early detection of abnormalities, for scheduling specific follow up and for suggesting effective therapeutic protocols. Copyright © 2017 Elsevier Inc. All rights reserved.

Ultrasonographic findings of breast lesions

International Nuclear Information System (INIS)

Hwang, In Sung; Kim, Yang Soo; Suh, Hyoung Sim

1990-01-01

Authors retrospectively analyzed ultrasonographic findings of 61 cases of breast lesions which were proven pathologically at Daerim St. Mary's Hospital from May 1987 to February 1990. The results were as follows : 1. Of all 61 cases, there were 27 fibroadenomas, 13 fibrocystic diseases, 11 carcinomas, 8 abscesses, 1 sclerosing adenosis, and 1 intraductal papilloma. 2. Findings suggesting benignancy were smooth contour, round or oval shape, homogeneously echolucent internal echo, echogenic boundary echo, and posterior enhancement. In the cases of abscess, the findings were rather irregular contour, strong posterior enhancement, and dirty, inhomogeneous internal echo. While irregular and lobulated shape, inhomogeneous and mixed internal echo and pectoral muscle invasion were suggested for malignancy. 3. The sensitivity was 98% and the specificity 58% in benign mass excluding abscesses, 63% and 98% in abscesses, and 55% and 98% in carcinomas. In conclusion, ultrasonography is one of the excellent imaging modality for detecting breast lesions larger than 5 mm in size, but unfortunately some of the malignant tumors simulated benignancy, thus we considered fine needle aspiration biopsy and adjunctive imaging modalities such as film mammography must be followed for better detection of breast cancer

Ultrasonographic findings of breast lesions

Energy Technology Data Exchange (ETDEWEB)

Hwang, In Sung; Kim, Yang Soo; Suh, Hyoung Sim [College of Medicine, Daerim St. Mary' s Hospital, Seoul (Korea, Republic of)

1990-07-15

Authors retrospectively analyzed ultrasonographic findings of 61 cases of breast lesions which were proven pathologically at Daerim St. Mary's Hospital from May 1987 to February 1990. The results were as follows : 1. Of all 61 cases, there were 27 fibroadenomas, 13 fibrocystic diseases, 11 carcinomas, 8 abscesses, 1 sclerosing adenosis, and 1 intraductal papilloma. 2. Findings suggesting benignancy were smooth contour, round or oval shape, homogeneously echolucent internal echo, echogenic boundary echo, and posterior enhancement. In the cases of abscess, the findings were rather irregular contour, strong posterior enhancement, and dirty, inhomogeneous internal echo. While irregular and lobulated shape, inhomogeneous and mixed internal echo and pectoral muscle invasion were suggested for malignancy. 3. The sensitivity was 98% and the specificity 58% in benign mass excluding abscesses, 63% and 98% in abscesses, and 55% and 98% in carcinomas. In conclusion, ultrasonography is one of the excellent imaging modality for detecting breast lesions larger than 5 mm in size, but unfortunately some of the malignant tumors simulated benignancy, thus we considered fine needle aspiration biopsy and adjunctive imaging modalities such as film mammography must be followed for better detection of breast cancer.

Reliability of Ultrasonographic Measurement of Cervical Multifidus Muscle Dimensions during Isometric Contraction of Neck Muscles

Directory of Open Access Journals (Sweden)

Somayeh Amiri Arimi

2012-07-01

Full Text Available Background and Aim: Cervical multifidus is considered as one of the most important neck stabilizers. Weakness and muscular atrophy of this muscle were seen in patients with chronic neck pain. Ultrasonographic imaging is a non-invasive and feasible technique that commonly used to record such changes and measure muscle dimensions. Therefore, the aim of this study was to evaluate the reliability of ultrasonographic measurement of cervical multifidus muscle’s dimensions during isometric contraction of neck muscles. Materials and Method: Ten subjects (5 patients with chronic neck pain and 5 healthy subjects were recruited in this study. Cervical multifidus muscle’s dimensions were measured at the level of forth cervical vertebrae. Ultrasonographic measurement of cervical multifidus muscle at rest, 50% and 100% of maximal voluntary contraction (MVC were performed by one examiner within 1 week interval. The dimensions of cervical multifidus muscle including cross-sectional area (CSA, anterior posterior dimension (APD, and lateral dimension (LD were measured. Intraclass correlation coefficients (ICC, standard error of measurement (SEM and minimal detectable change (MDC were computed for data analysis.Results: The between days reliability of maximum strength of neck muscles and multifidus muscle dimensions at rest, 50% and 100% of MVC of neck muscles were good to excellent (ICC=0.75-0.99.Conclusion: The results of this study showed that ultrasonographic measuring of cervical multifidus muscle’s dimensions during isometric contraction of neck muscles at the level of C4 in females with chronic neck pain and healthy subjects is a reliable and repeatable method.

Soft Tissue Masses in the Extremities: The Accuracy of an Ultrasonographic Diagnosis

Energy Technology Data Exchange (ETDEWEB)

An, Ji Young; Park, So Young; Park, Ji Seon; Jin, Wook; Ryu, Kyung Nam [Kyung Hee University Medical Center, Seoul (Korea, Republic of)

2011-09-15

We wanted to retrospectively determine the accuracy of an ultrasonographic diagnosis of superficial soft tissue masses in the extremities by using the histologic results as the reference standard. From January 2005 to June 2010, 154 patients with soft tissue masses in the extremities and who underwent ultrasonographic evaluation followed by biopsy or resection were retrospectively evaluated. The ultrasonographic and histologic diagnoses of the soft tissue masses were lipoma, ganglion cyst, hemangioma, neurogenic tumor, giant cell tumor of the tendon sheath, epidermoid cyst, fibroma, glomus tumor, Baker's cyst and neurofibromatosis. Out of 154 patients, 114 (74%) patients showed concordance between the histologic diagnosis and the ultrasonographic diagnosis, and the remaining 40 (26%) patients did not. The diagnostic accuracy of each soft tissue mass was 95% for lipoma, 83% for ganglion cyst, 75% for hemangioma, 72% for neurogenic tumor, 50% for giant cell tumor of the tendon sheath, 43% for epidermoid cyst, 33% for fibroma and 100% each for glomus tumor, fibromatosis and Baker's cyst. Aside from these tumors, there were also sarcoma, malignant melanoma, elastofibroma, Kimura disease and pilomatricoma. Among the cases that showed discordance between the histologic diagnosis and the ultrasonographic diagnosis, three of them were notable; pilomatricoma being misdiagnosed as dermatofibroma protuberans, angiolipoma being misdiagnosed as vascular leiomyoma and malignant fibrous histiocytoma being misdiagnosed as a malignant soft tissue mass. The accuracy of an ultrasonographic diagnosis for soft tissue masses in the extremities varies greatly according to each type of mass. Lipoma, ganglion cyst, hemangioma, glomus tumor, neurogenic tumor and Baker's cyst showed a relatively high rate of concordance between the ultrasonographic diagnosis and the histologic diagnosis, but epidermoid cyst and fibroma showed a relatively lower rate of concordance

External Validation of Fatty Liver Index for Identifying Ultrasonographic Fatty Liver in a Large-Scale Cross-Sectional Study in Taiwan

Science.gov (United States)

Fang, Kuan-Chieh; Wang, Yuan-Chen; Huo, Teh-Ia; Huang, Yi-Hsiang; Yang, Hwai-I; Su, Chien-Wei; Lin, Han-Chieh; Lee, Fa-Yauh; Wu, Jaw-Ching; Lee, Shou-Dong

2015-01-01

Background and Aims The fatty liver index (FLI) is an algorithm involving the waist circumference, body mass index, and serum levels of triglyceride and gamma-glutamyl transferase to identify fatty liver. Although some studies have attempted to validate the FLI, few studies have been conducted for external validation among Asians. We attempted to validate FLI to predict ultrasonographic fatty liver in Taiwanese subjects. Methods We enrolled consecutive subjects who received health check-up services at the Taipei Veterans General Hospital from 2002 to 2009. Ultrasonography was applied to diagnose fatty liver. The ability of the FLI to detect ultrasonographic fatty liver was assessed by analyzing the area under the receiver operating characteristic (AUROC) curve. Results Among the 29,797 subjects enrolled in this study, fatty liver was diagnosed in 44.5% of the population. Subjects with ultrasonographic fatty liver had a significantly higher FLI than those without fatty liver by multivariate analysis (odds ratio 1.045; 95% confidence interval, CI 1.044–1.047, pfatty liver (AUROC: 0.827, 95% confidence interval, 0.822–0.831). An FLI fatty liver. Moreover, an FLI ≥ 35 (positive likelihood ratio (LR+) 3.12) for males and ≥ 20 (LR+ 4.43) for females rule in ultrasonographic fatty liver. Conclusions FLI could accurately identify ultrasonographic fatty liver in a large-scale population in Taiwan but with lower cut-off value than the Western population. Meanwhile the cut-off value was lower in females than in males. PMID:25781622

Neural network approach to radiologic lesion detection

International Nuclear Information System (INIS)

Newman, F.D.; Raff, U.; Stroud, D.

1989-01-01

An area of artificial intelligence that has gained recent attention is the neural network approach to pattern recognition. The authors explore the use of neural networks in radiologic lesion detection with what is known in the literature as the novelty filter. This filter uses a linear model; images of normal patterns become training vectors and are stored as columns of a matrix. An image of an abnormal pattern is introduced and the abnormality or novelty is extracted. A VAX 750 was used to encode the novelty filter, and two experiments have been examined

The ultrasonographic findings of acute pelvic inflammatory disease

International Nuclear Information System (INIS)

Choi, Yeon Nam; Park, Jai Soung; Lee, Hae Kyung; Chung, Moo Chan; Lee, Beong Ho; Kim, Ki Jung

1987-01-01

Although ultrasonographic findings of female pelvic mass are frequently reported, those of acute PID are not well established. But differentiation of fluid collection and mass lesion of PID is exactly made by ultrasonography. We analysed the ultrasonographic findings in 26 cases of acute PID having satisfactory operative or histological proofs, examined at Soonchunhyang University Hospital from Oct. 1985 to Feb. 1987. The results were as follows: 1. The age was ranged from 17 years to 53 years of age and the majority was between 21 years and 50 years of age. 2. Ultrasonographic findings are classified to fluid collection in cul de sac 17, tuboovarian abscess, 7 pyosalpix 2, endometritis 1 and normal 2 cases. 3. In cases of pelvic mass formation, their ecnogenecity were cystic form in 6 cases (22%), mixed form in 19 cases (71%), solid form in 2 cases (7%), and shapes were mainly ovoid with irregular, ill-defined margin. The location of mass is unilateral in 17 cases (63%) bilateral in 10 cases (37%)

Artificial-neural-network-based failure detection and isolation

Science.gov (United States)

Sadok, Mokhtar; Gharsalli, Imed; Alouani, Ali T.

1998-03-01

This paper presents the design of a systematic failure detection and isolation system that uses the concept of failure sensitive variables (FSV) and artificial neural networks (ANN). The proposed approach was applied to tube leak detection in a utility boiler system. Results of the experimental testing are presented in the paper.

Taller-than-wide sign for predicting thyroid microcarcinoma: comparison and combination of two ultrasonographic planes.

Science.gov (United States)

Chen, Shun-Ping; Hu, Yuan-Ping; Chen, Bin

2014-09-01

The aims of this study were to investigate the accuracy of using the taller-than-wide (TTW) sign in two ultrasonographic planes to predict thyroid microcarcinoma, and to confirm the hypothesis that the presence of a TTW sign in both the transverse and longitudinal ultrasonographic planes strongly suggests thyroid microcarcinoma. Nine hundred forty-two thyroid nodules ≤1 cm were submitted to surgical-histopathologic and ultrasonographic examination. TTW signs were divided into three types based on their detection only in the transverse plane (TTTW type, n = 100), only in the longitudinal plane (LTTW type, n = 61) or in both planes (BTTW type, n = 131). The areas under the receiver operating characteristic curves (A(z)) for the three different TTW signs, as well as for the combination of all TTW signs (ATTW, n = 292), were compared. The results indicated that the A(z) values of the TTTW, LTTW, BTTW and ATTW signs in predicting thyroid microcarcinoma were 0.544, 0.531, 0.627 and 0.702, respectively. The ATTW sign was the most accurate (p 0.05). Therefore, both the LTTW and TTTW signs are reliable markers of thyroid microcarcinoma. The BTTW sign strongly suggests thyroid microcarcinoma. Copyright © 2014 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Radiographic and ultrasonographic characteristics of ventral abdominal hernia in pigeons (Columba livia).

Science.gov (United States)

Amer, Mohammed S; Hassan, Elham A; Torad, Faisal A

2018-02-20

Five female egg-laying pigeons presented with painless, reducible, ventral abdominal swellings located between the keel and the pubis, or close to the cloaca. Based on clinical, radiographic, and ultrasonographic examination, these pigeons were diagnosed with ventral abdominal hernia requiring surgical interference. Reduction was successfully performed under general anesthesia. Radiographic and ultrasonographic examinations were beneficial for confirming the diagnosis and visualizing the hernial content for surgical planning. Lateral radiographs were more helpful than ventrodorsal radiographs for identification of the hernial content and its continuation with the abdominal muscles. Ultrasonographic examination offered a non-invasive diagnostic tool that allowed for the differentiation of hernia from other abdominal swellings. In addition, it played a beneficial role in identification of the hernial content and follow up after surgical interference. In conclusion, radiographic and ultrasonographic examinations were beneficial in the diagnosis, surgical planning, and follow up after surgical interference of ventral abdominal hernia in pigeons.

Deep Neural Network Detects Quantum Phase Transition

Science.gov (United States)

Arai, Shunta; Ohzeki, Masayuki; Tanaka, Kazuyuki

2018-03-01

We detect the quantum phase transition of a quantum many-body system by mapping the observed results of the quantum state onto a neural network. In the present study, we utilized the simplest case of a quantum many-body system, namely a one-dimensional chain of Ising spins with the transverse Ising model. We prepared several spin configurations, which were obtained using repeated observations of the model for a particular strength of the transverse field, as input data for the neural network. Although the proposed method can be employed using experimental observations of quantum many-body systems, we tested our technique with spin configurations generated by a quantum Monte Carlo simulation without initial relaxation. The neural network successfully identified the strength of transverse field only from the spin configurations, leading to consistent estimations of the critical point of our model Γc = J.

Ultrasonographic findings in goats with contagious caprine pleuropneumonia caused by Mycoplasma capricolum subsp. capripneumoniae.

Science.gov (United States)

Tharwat, Mohamed; Al-Sobayil, Fahd

2017-08-22

In goats, contagious caprine pleuropneumonia (CCPP) is a cause of major economic losses in Africa, Asia and in the Middle East. There is no information emphasising the importance of diagnostic ultrasound in goats with CCPP caused by Mycoplasma capricolum subsp. capripneumoniae (Mccp). This study was designed to describe the ultrasonographic findings in goats with CCPP caused by Mccp and to correlate ultrasonographic with post-mortem findings. To this end, 55 goats with CCPP were examined. Twenty-five healthy adult goats were used as a control group. Major clinical findings included harried, painful respiration, dyspnoea and mouth breathing. On ultrasonography, a liver-like echotexture was imaged in 13 goats. Upon post-mortem examination, all 13 goats exhibited unilateral pulmonary consolidation. Seven goats had a unilateral hypoechoic pleural effusion. At necropsy, the related lung was consolidated and the pleural fluid appeared turbid and greenish. Pleural abscessiation detected in five goats was confirmed post-mortem. Twenty-eight goats had a bright, fibrinous matrix extending over the chest wall containing numerous anechoic fluid pockets with medial displacement and compression of lung tissue. Echogenic tags imaged floating in the fluid were found upon post-mortem examination to be fibrin. In two goats, a consolidated right parenchyma was imaged together with hypoechoic pericardial effusions with echogenic tags covering the epicardium. At necropsy, the right lung was consolidated in three goats and fibrin threads were found covering the epicardium and pericardium. In goats with CCPP, the extension and the severity of the pulmonary changes could not be verified with clinical certainty in most cases, whereas this was possible most of the time with sonography, thus making the prognosis easier. Ultrasonographic examination of the pleurae and the lungs helped in the detection of various lesions.

Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene.

Science.gov (United States)

Li, Jun; Mei, Xue; Prokhorov, Danil; Tao, Dacheng

2017-03-01

Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for successful analysis. This paper extends the framework of deep neural networks by accounting for the structural cues in the visual signals. In particular, two kinds of neural networks have been proposed. First, we develop a multitask deep convolutional network, which simultaneously detects the presence of the target and the geometric attributes (location and orientation) of the target with respect to the region of interest. Second, a recurrent neuron layer is adopted for structured visual detection. The recurrent neurons can deal with the spatial distribution of visible cues belonging to an object whose shape or structure is difficult to explicitly define. Both the networks are demonstrated by the practical task of detecting lane boundaries in traffic scenes. The multitask convolutional neural network provides auxiliary geometric information to help the subsequent modeling of the given lane structures. The recurrent neural network automatically detects lane boundaries, including those areas containing no marks, without any explicit prior knowledge or secondary modeling.

Symptomatic and asymptomatic interphalageal osteoarthritis: An ultrasonographic study.

Science.gov (United States)

Usón, Jacqueline; Fernández-Espartero, Cruz; Villaverde, Virginia; Condés, Emilia; Godo, Javier; Martínez-Blasco, Maria Jesus; Miguélez, Roberto

2014-01-01

To date few studies have examined whether ultrasonography can depict morphologic differences in painful and painless osteoarthritis (OA). This study describes and compares the clinical, radiographic and ultrasonographic findings of patients with both painful and painless proximal interphalgeal (PIP) and/or distal interphalgeal (DIP) OA. Patients with PIP and/or DIP OA (ACR criteria) were prospectively recruited. The clinical rheumatologist chose up to 3 painful joints and up to 3 painless symmetric joints in each patient to define 2 cohorts of OA: symptomatic (SG) and asymptomatic (ASG). A conventional postero-anterior hand x ray was performed and read by one rheumatologist following the OARSI atlas, blinded to clinical and sonographic data. Ultrasound (US) was performed by an experienced rheumatologist, blinded to both clinical and radiographic data in joints previously selected by the clinical rheumatologist. US-pathology was assessed as present or absent as defined in previous reports: osteophytes, joint space narrowing, synovitis, intra-articular power doppler signal, intra-articular bony erosion, and visualization of cartilage. Radiographic and ultrasonographic intrareader reliability test was performed. A total of 50 joints in the SG and ASG were included from 20 right handed women aged 61.85 (46-73) years with PIP and DIP OA diagnosed 6.8 (1-17) years ago. 70% SG joints and ASG were right and left sided respectively. The SG showed significantly more osteophytes, synovitis and non-visualization of joint cartilage. Intrareader radiographic and ultrasonographic agreement was excellent. This study demonstrates that painful PIP and/or DIP OA have more ultrasonographic structural changes and synovitis. Copyright © 2013 Elsevier España, S.L.U. All rights reserved.

Feline alimentary lymphosarcoma: radiographic, ultrasonographic, histologic, and viral findings

International Nuclear Information System (INIS)

Hittmair, K.; Krebitz-Gressl, E.; Kuebber-Heiss, A.; Moestl, K.

2000-01-01

Sixty cats with clinical symptoms indicative of gastroin-testinal lymphosarcoma were examined radiographically and ultrasonographically. Clinical signs included lethargy, anorexia, weight loss, anemia, vomiting, diarrhea, and a palpable mid-abdominal mass. Radiographic findings with alimentary lymphosarcoma (LSA) showed diffuse decreased serosal detail, a mid-abdominal soft-tissue mass, cavernous lesions, and gas-filled bowel loops. Ultrasonographic features included marked stomach or intestinal wall thickening, loss of wall layering, decreased echogenicity, and a hyperechoic central reflection. Hypoechonic infiltration of mesenterial lymph nodes and other abdominal organs were visualized ultrasonographically. Alimentary LSA was diagnosed in thirty-six of the sixty cats. Ultrasonography was helpful in determining the cause of disease in the remaining twenty-four cats. Differential diagnosis included intussusception, foreign bodies, chronic gastroenteritis, granuloma (feline infectious peritonitis - FIP), and other gastrointestinal neoplasms. In ten of the thirty-six cats with alimentary lymphosarcoma, diagnosis was confirmed by ultrasound-guided fine-needle biopsies. Blood and/or saliva ELISA-tests determined feline leukemia virus or antigen in only eleven of the thirty-six cats. Histopathology revealed lymphoid infiltration of the stomach or intestinal wall in twenty-nine of the thirty-six cases. Additionally, the medical records of seventy-one cats with proven alimentary LSA were reviewed. Ultrasonographic findings showed intestinal LSA in sixty-two cats and LSA of the stomach in nine cats. Both studies indicate that ultrasonography is a valuable diagnostic tool for feline alimentary LSA. (author)

Ultrasonographic, endoscopic and histological appearance of the caecum in clinically healthy cats.

Science.gov (United States)

Hahn, Harriet; Freiche, Valérie; Baril, Aurélie; Charpentier, Julie; Desquilbet, Loïc; Le Poder, Sophie; Servely, Jean-Luc; Laloy, Eve; Pey, Pascaline

2017-02-01

Objectives The aim of the study was to describe the ultrasonographic and endoscopic appearance and characteristics of the caecum in asymptomatic cats, and to correlate these findings with histology. Methods Ex vivo ultrasonographic and histologic evaluations of a fresh caecum were initially performed. Then, 20 asymptomatic cats, privately owned or originating from a reproductive colony, were recruited. All cats had an ultrasonographic examination of the ileocaecocolic junction, where the thickness of the caecal wall, ileocolic lymph nodes and the echogenicity of the local fat were assessed. They all underwent a colonoscopy with a macroscopic assessment of the mucosa and biopsies for histology. Results An ultrasonographic hypoechoic nodular inner layer, which corresponded to the coalescence of multiple lymphoid follicles originating from the submucosa and protruding in the mucosa on histology, was visible in all parts of the caecum. The combined mucosa and submucosa was measured ultrasonographically and defined as the follicular layer. Although all cats were asymptomatic, 3/19 cats showed mild caecal inflammation on histology. The most discriminatory ultrasonographic parameter in assessing this subclinical inflammation was the thickness of the follicular layer at the entrance of the caecum, with a cut-off value of 2.0 mm. All cats (20/20) showed some degree of macroscopic 'dimpling' of the caecal mucosa on endoscopy. Conclusions and relevance Lymphoid follicles in the caecal mucosa and submucosa constitute a unique follicular layer on ultrasound. In asymptomatic cats, a subtle, non-clinically relevant inflammation may exist and this is correlated with an increased thickness of the follicular layer on ultrasound. On endoscopy, a 'dimpled aspect' to the caecal mucosa is a normal finding in the asymptomatic cat.

Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

Fen Chen

2018-03-01

Full Text Available Fast and automatic detection of airports from remote sensing images is useful for many military and civilian applications. In this paper, a fast automatic detection method is proposed to detect airports from remote sensing images based on convolutional neural networks using the Faster R-CNN algorithm. This method first applies a convolutional neural network to generate candidate airport regions. Based on the features extracted from these proposals, it then uses another convolutional neural network to perform airport detection. By taking the typical elongated linear geometric shape of airports into consideration, some specific improvements to the method are proposed. These approaches successfully improve the quality of positive samples and achieve a better accuracy in the final detection results. Experimental results on an airport dataset, Landsat 8 images, and a Gaofen-1 satellite scene demonstrate the effectiveness and efficiency of the proposed method.

Multimodal ultrasonographic assessment of leiomyosarcoma of the femoral vein in a patient misdiagnosed as having deep vein thrombosis

Science.gov (United States)

Zhang, Mei; Yan, Feng; Huang, Bin; Wu, Zhoupeng; Wen, Xiaorong

2017-01-01

Abstract Rationale: Primary leiomyosarcoma (LMS) of the vein is a rare tumor that arises from the smooth muscle cells of the vessel wall and has an extremely poor prognosis. This tumor can occur in vessels such as the inferior vena cava, great saphenous vein, femoral vein, iliac vein, popliteal vein, and renal vein; the inferior vena cava is the most common site. LMS of the femoral vein can result in edema and pain in the lower extremity; therefore, it is not easy to be differentiated from deep vein thrombosis (DVT). Moreover, virtually no studies have described the ultrasonographic features of LMS of the vein in detail. Patient concerns: We present a case of a 55-year-old woman with LMS of the left femoral vein that was misdiagnosed as having deep vein thrombosis (DVT) on initial ultrasonographic examination. The patient began to experience edema and pain in her left leg seven months previously. She was diagnosed as having DVT on initial ultrasonographic examination, but the DVT treatment that she had received for 7 months failed to improve the status of her left lower limb. Diagnoses: She subsequently underwent re-examination by means of a multimodal ultrasonographic imaging approach (regular B-mode imaging, color Doppler imaging, pulsed-wave Doppler imaging, contrast-enhanced ultrasonography), which confirmed a diagnosis of LMS. Interventions: This patient was treated successfully with surgery. Outcomes: This case demonstrates that use of multiple ultrasonographic imaging techniques can be helpful to diagnose LMS accurately. Detection of vasculature in a dilated vein filled with a heterogeneous hypoechoic substance on ultrasonography is a sign of a tumor. Lessons: The pitfall of misdiagnosing this tumor as DVT is a useful reminder. PMID:29145269

SPR imaging combined with cyclic voltammetry for the detection of neural activity

Directory of Open Access Journals (Sweden)

Hui Li

2014-03-01

Full Text Available Surface plasmon resonance (SPR detects changes in refractive index at a metal-dielectric interface. In this study, SPR imaging (SPRi combined with cyclic voltammetry (CV was applied to detect neural activity in isolated bullfrog sciatic nerves. The neural activities induced by chemical and electrical stimulation led to an SPR response, and the activities were recorded in real time. The activities of different parts of the sciatic nerve were recorded and compared. The results demonstrated that SPR imaging combined with CV is a powerful tool for the investigation of neural activity.

Definition and Reliability Assessment of Elementary Ultrasonographic Findings in Calcium Pyrophosphate Deposition Disease

DEFF Research Database (Denmark)

Filippou, Georgios; Scirè, Carlo A; Damjanov, Nemanja

2017-01-01

OBJECTIVE: To define the ultrasonographic characteristics of calcium pyrophosphate crystal (CPP) deposits in joints and periarticular tissues and to evaluate the intra- and interobserver reliability of expert ultrasonographers in the assessment of CPP deposition disease (CPPD) according to the ne...

Clinical and ultrasonographic findings of some ocular conditions in sheep and goats

Directory of Open Access Journals (Sweden)

O. El-Tookhy

2013-01-01

Full Text Available This study was carried out to describe the ultrasonographic findings in relation to the clinical symptoms of some common ocular conditions in sheep and goats. Fifty animals (32 goats and 18 sheep with different ocular problems were examined. Ultrasonographic examination was performed using a B-mode ocular ultrasound unit, and the structure of the globe was evaluated at a depth of 4-6 cm. Early cases (n=35, 70% showed varying ocular conditions; hypopyon, (n=8, 16%, stromal abscesses, (n=4, 8%, and anterior uveitis (n=23, 46%. Hypopyon appeared clinically as a white or yellowish material in the anterior chamber, and ultrasonographically as a hyperechoic mass in the anterior chamber. Severe iridocyclitis was noticed in acute cases of infectious keratoconjunctivitis (IKC accompanied by blepharospasm, photophobia, excessive tearing and eyelid margin crust formation. Ultrasonographically, the pupil appeared constricted with increased hyperechoic thickening of the ciliary body. In chronic cases of IKC, corneal pigmentation (n=5, 10% and cataract (n=10, 20% were seen. Ultrasonographically the type and degree of cataract were diagnosed. The present study provides an inside view of the inner ocular structures during the course of certain eye diseases where ophthalmoscopic examination is not possible. Our findings, although preliminary, are relevant for the more complete diagnosis of certain external ocular conditions in sheep and goat herds.

Medical management of first trimester miscarriage according to ultrasonographic findings

DEFF Research Database (Denmark)

Vejborg, Thomas; Nilas, Lisbeth; Rørbye, Christina

2007-01-01

BACKGROUND: The efficacy of medical treatment of first trimester miscarriages may depend on the regimen used, the definition of success, clinical symptoms, and, possibly, on the ultrasonographic findings. Our primary aim was to assess if a single dose of misoprostol could reduce the number of sur...... of pregnancy failure, time of assessment, and the criteria for success.......BACKGROUND: The efficacy of medical treatment of first trimester miscarriages may depend on the regimen used, the definition of success, clinical symptoms, and, possibly, on the ultrasonographic findings. Our primary aim was to assess if a single dose of misoprostol could reduce the number...... ultrasonography after either 1, 2 or 3 days. Treatment was successful if a complete abortion was diagnosed at follow-up. The women were divided into 4 ultrasonographically-defined groups: missed abortion with a crown rump length (CRL)>or=6 mm (Group A1) or CRL

Clinical and ultrasonographic results of ultrasonographically guided percutaneous radiofrequency lesioning in the treatment of recalcitrant lateral epicondylitis.

Science.gov (United States)

Lin, Cheng-Li; Lee, Jung-Shun; Su, Wei-Ren; Kuo, Li-Chieh; Tai, Ta-Wei; Jou, I-Ming

2011-11-01

In patients with lateral epicondylitis recalcitrant to nonsurgical treatments, surgical intervention is considered. Despite the numerous therapies reported, the current trend of treatment places particular emphasis on minimally invasive techniques. The authors present a newly developed minimally invasive procedure, ultrasonographically guided percutaneous radiofrequency thermal lesioning (RTL), and its clinical efficacy in treating recalcitrant lateral epicondylitis. Level of evidence, 4. Thirty-four patients (35 elbows), with a mean age of 52.1 years (range, 35-65 years), suffered from symptomatic lateral epicondylitis for more than 6 months and had exhausted nonoperative therapies. They were treated with ultrasonographically guided RTL. Patients were followed up at least 6 months by physical examination and 12 months by interview. The intensity of pain was recorded with a visual analog scale (VAS) score. The functional outcome was evaluated using grip strength, the upper limb Disability of Arm, Shoulder and Hand (QuickDASH) outcome measure, and the Modified Mayo Clinic Performance Index (MMCPI) for the elbow. The ultrasonographic findings regarding the extensor tendon origin were recorded, as were the complications. At the time of the 6-month follow-up, the average VAS score in resting (from 4.9 to 0.9), palpation (from 7.6 to 2.5), and grip (from 8.2 to 2.9) had improved significantly compared with the preoperative condition (P lateral epicondylitis was found to be a minimally invasive treatment with satisfactory results in this pilot investigation. This innovative method can be considered as an alternative treatment of recalcitrant lateral epicondylitis before further surgical intervention.

ROAD DETECTION BY NEURAL AND GENETIC ALGORITHM IN URBAN ENVIRONMENT

Directory of Open Access Journals (Sweden)

A. Barsi

2012-07-01

Full Text Available In the urban object detection challenge organized by the ISPRS WG III/4 high geometric and radiometric resolution aerial images about Vaihingen/Stuttgart, Germany are distributed. The acquired data set contains optical false color, near infrared images and airborne laserscanning data. The presented research focused exclusively on the optical image, so the elevation information was ignored. The road detection procedure has been built up of two main phases: a segmentation done by neural networks and a compilation made by genetic algorithms. The applied neural networks were support vector machines with radial basis kernel function and self-organizing maps with hexagonal network topology and Euclidean distance function for neighborhood management. The neural techniques have been compared by hyperbox classifier, known from the statistical image classification practice. The compilation of the segmentation is realized by a novel application of the common genetic algorithm and by differential evolution technique. The genes were implemented to detect the road elements by evaluating a special binary fitness function. The results have proven that the evolutional technique can automatically find major road segments.

Ultrasonographic findings of nonlactiferous breast abscess

Energy Technology Data Exchange (ETDEWEB)

Hwang, Sung Su; Kim, Hak Hee; Lee, Myung Hee; Rho, Sang Chun; Jung, Seon Ok; Jung, So Leoung; Cha, Eun Sook; Shinn, Kyung Sub [Catholic University Medical College, Seoul (Korea, Republic of)

1995-04-15

To evaluate the ultrasonographic features of nonlactiferous breast abscess. We retrospectively reviewed ultrasonograms of 21 cases with surgically and clinically proved nonlactiferous breast abscess. The cases included 17 cases of acute or chronic inflammation and 4 cases of tuberculosis. Location of the lesion was subareolar in 15 cases and peripheral in 6. Mean anteroposterior/transverse diameter ratio was 0.49. Internal echogenicitiy of the lesion was variable, with heterogeneous mixed-echoic echotexture in 18 cases and homogeneous hypoechoic in 3. Margin of the lesion was irregular in 18 cases (85.7%) and posterior sonic enhancement was observed in 17 cases (81%). There were also noted obliteration of adjacent superficial fascia, localized skin thickening, and sinus tract or ductal ectasia in 19 (90.5%), 9 (42.9%), and 9(42.9%) cases respectively. Major ultrasonographic findings of nonlactiferous breast abscess was subareolar located, variable shaped mass with posterior enhancement. Additional findings were fistular formation, loss of superficial fascia, and axillary lymphadenopathy.

Ultrasonographic findings of nonlactiferous breast abscess

International Nuclear Information System (INIS)

Hwang, Sung Su; Kim, Hak Hee; Lee, Myung Hee; Rho, Sang Chun; Jung, Seon Ok; Jung, So Leoung; Cha, Eun Sook; Shinn, Kyung Sub

1995-01-01

To evaluate the ultrasonographic features of nonlactiferous breast abscess. We retrospectively reviewed ultrasonograms of 21 cases with surgically and clinically proved nonlactiferous breast abscess. The cases included 17 cases of acute or chronic inflammation and 4 cases of tuberculosis. Location of the lesion was subareolar in 15 cases and peripheral in 6. Mean anteroposterior/transverse diameter ratio was 0.49. Internal echogenicitiy of the lesion was variable, with heterogeneous mixed-echoic echotexture in 18 cases and homogeneous hypoechoic in 3. Margin of the lesion was irregular in 18 cases (85.7%) and posterior sonic enhancement was observed in 17 cases (81%). There were also noted obliteration of adjacent superficial fascia, localized skin thickening, and sinus tract or ductal ectasia in 19 (90.5%), 9 (42.9%), and 9(42.9%) cases respectively. Major ultrasonographic findings of nonlactiferous breast abscess was subareolar located, variable shaped mass with posterior enhancement. Additional findings were fistular formation, loss of superficial fascia, and axillary lymphadenopathy

Fungal myositis in children: serial ultrasonographic findings

Energy Technology Data Exchange (ETDEWEB)

Kwon, Jung Hwa; Lee, Hee Jung; Choi, Jin Soo [Keimyung University School of Medicine, Daegu (Korea, Republic of)

2003-08-01

To evaluate serial ultrasonographic findings of fungal myositis in children. Eleven lesions caused by fungal myositis and occurring in six children were included in this study. Eight lesions in five children were histopathologically proven and the other three were clinically diagnosed. Serial ultrasonographic findings were retrospectively evaluated in terms of size, location, margin, internal echotexture and adjacent cortical change occurring during the follow-up period ranging from five days to two months. Three patients (50%) had multiple lesions. The sites of involvment were the thigh (n=4), calf (n=3), chest wall (n=2), abdominal wall (n=1) and forearm (n=1). Initially, diffuse muscular swelling was revealed, with ill-defined hypoechoic lesions confined to the muscle layer (n=8). Follow-up examination of eight lesions over a period of 5-10 days showed that round central echogenic lesions were surrounded by previous slightly echogenic lesions (n=6, 75%). Long-term follow-up of five lesions over a two-month period revealed periosteal thickening in one case (20%), and the peristence of echogenic solid nodules in four (80%). Pathologic examination showed that the central lesions correlated with a fungus ball and the peripheral slightly echogenic lesions corresponded to hematoma and necrosis. Serial ultrasonographic findings of fungal myositis in children revealed relatively constant features in each case. In particular, the findings of muscular necrosis and a fungus ball over a period of 5-14 days were thought to be characteristic.

Fungal myositis in children: serial ultrasonographic findings

International Nuclear Information System (INIS)

Kwon, Jung Hwa; Lee, Hee Jung; Choi, Jin Soo

2003-01-01

To evaluate serial ultrasonographic findings of fungal myositis in children. Eleven lesions caused by fungal myositis and occurring in six children were included in this study. Eight lesions in five children were histopathologically proven and the other three were clinically diagnosed. Serial ultrasonographic findings were retrospectively evaluated in terms of size, location, margin, internal echotexture and adjacent cortical change occurring during the follow-up period ranging from five days to two months. Three patients (50%) had multiple lesions. The sites of involvment were the thigh (n=4), calf (n=3), chest wall (n=2), abdominal wall (n=1) and forearm (n=1). Initially, diffuse muscular swelling was revealed, with ill-defined hypoechoic lesions confined to the muscle layer (n=8). Follow-up examination of eight lesions over a period of 5-10 days showed that round central echogenic lesions were surrounded by previous slightly echogenic lesions (n=6, 75%). Long-term follow-up of five lesions over a two-month period revealed periosteal thickening in one case (20%), and the peristence of echogenic solid nodules in four (80%). Pathologic examination showed that the central lesions correlated with a fungus ball and the peripheral slightly echogenic lesions corresponded to hematoma and necrosis. Serial ultrasonographic findings of fungal myositis in children revealed relatively constant features in each case. In particular, the findings of muscular necrosis and a fungus ball over a period of 5-14 days were thought to be characteristic

The validity of ultrasonographic scanning as screening method for abdominal aortic aneurysm

DEFF Research Database (Denmark)

Lindholt, Jes Sanddal; Vammen, Sten; Juul, Søren

1999-01-01

the sensitivity and specificity of screening for abdominal aortic aneurysms (AAAs) with ultrasonographic scanning (US) is unknown. The aim of the study was to validate US as screening test for AAAs.......the sensitivity and specificity of screening for abdominal aortic aneurysms (AAAs) with ultrasonographic scanning (US) is unknown. The aim of the study was to validate US as screening test for AAAs....

Imaging diagnosis--ultrasonographic appearance of small bowel metastasis from canine mammary carcinoma.

Science.gov (United States)

Domínguez, Elisabet; Anadón, Eduard; Espada, Yvonne; Grau-Roma, Llorenç; Majó, Natàlia; Novellas, Rosa

2014-01-01

A 10-year-old entire female Beagle dog was evaluated for an acute history of lethargy, anorexia, and diarrhea. Mammary tumors were detected during physical examination. Ultrasonographic scanning revealed the presence of a unique pattern of multiple, well-defined and well-marginated hypoechoic nodules in the muscularis layer of the jejunum. These nodules were not associated with changes in the rest of the normal intestinal layering and were not causing signs of intestinal obstruction. Mammary carcinoma metastases to the intestinal muscularis layer were diagnosed based on histopathological examination. © 2013 American College of Veterinary Radiology.

Ultrasonographic findings in patients with nonbacterial prostatitis

NARCIS (Netherlands)

de la Rosette, J. J.; Karthaus, H. F.; Debruyne, F. M.

1992-01-01

The potential value of prostatic imaging in the diagnosis of inflammatory disorders of the prostate is largely unexplored. In several studies, specific ultrasonographic characteristics in patients with prostatitis have been described. Also nonspecific echogenic qualities in prostatitis have been

Usefulness of ultrasonographic examination of diagnosis of muscle hernia

International Nuclear Information System (INIS)

Choi, Jin Soo; Lee, Sung Moon

2003-01-01

To evaluate the usefulness of ultrasonography in diagnosis of muscle hernia. Ultrasonographic findings of seven patients with muscle hernia were retrospectively reviewed. The subjects consisted of 6 males and 1 female, age ranged from 17 to 66 years (mean=45 years). Ultrasonographic examination was performed using a high-frequency (7-15 MHz) linear probe during rest and stress states of the affected muscle, and both tranverse and longitudinal views were obtained. Six muscle herniations were located in the lower extremity in six cases while only one muscle herniation, in the upper extremity. Four cases showed a focal defect of the fascia with a localized bulging out of the muscle substance through the defect. Herniated muscle in stress state was larger and harder than in rest state. In 3 cases, defect of the fascia was not noted on ultrasonography. However, the affected muscle showed an abnormal contraction with a focal bulging out appearance during stress state. Ultrasonographically, the herniated muscle substance was less echogenic than the normal muscle without any evidence of muscle tear or associated mass in all cases. Ultrasonography is a simple and useful dynamic study of muscle hernia in diagnosis and differentiation of muscle hernia.

Multimodal ultrasonographic assessment of leiomyosarcoma of the femoral vein in a patient misdiagnosed as having deep vein thrombosis: A case report.

Science.gov (United States)

Zhang, Mei; Yan, Feng; Huang, Bin; Wu, Zhoupeng; Wen, Xiaorong

2017-11-01

Primary leiomyosarcoma (LMS) of the vein is a rare tumor that arises from the smooth muscle cells of the vessel wall and has an extremely poor prognosis. This tumor can occur in vessels such as the inferior vena cava, great saphenous vein, femoral vein, iliac vein, popliteal vein, and renal vein; the inferior vena cava is the most common site. LMS of the femoral vein can result in edema and pain in the lower extremity; therefore, it is not easy to be differentiated from deep vein thrombosis (DVT). Moreover, virtually no studies have described the ultrasonographic features of LMS of the vein in detail. We present a case of a 55-year-old woman with LMS of the left femoral vein that was misdiagnosed as having deep vein thrombosis (DVT) on initial ultrasonographic examination. The patient began to experience edema and pain in her left leg seven months previously. She was diagnosed as having DVT on initial ultrasonographic examination, but the DVT treatment that she had received for 7 months failed to improve the status of her left lower limb. She subsequently underwent re-examination by means of a multimodal ultrasonographic imaging approach (regular B-mode imaging, color Doppler imaging, pulsed-wave Doppler imaging, contrast-enhanced ultrasonography), which confirmed a diagnosis of LMS. This patient was treated successfully with surgery. This case demonstrates that use of multiple ultrasonographic imaging techniques can be helpful to diagnose LMS accurately. Detection of vasculature in a dilated vein filled with a heterogeneous hypoechoic substance on ultrasonography is a sign of a tumor. The pitfall of misdiagnosing this tumor as DVT is a useful reminder.

Detection and recognition of bridge crack based on convolutional neural network

Directory of Open Access Journals (Sweden)

Honggong LIU

2016-10-01

Full Text Available Aiming at the backward artificial visual detection status of bridge crack in China, which has a great danger coefficient, a digital and intelligent detection method of improving the diagnostic efficiency and reducing the risk coefficient is studied. Combing with machine vision and convolutional neural network technology, Raspberry Pi is used to acquire and pre-process image, and the crack image is analyzed; the processing algorithm which has the best effect in detecting and recognizing is selected; the convolutional neural network(CNN for crack classification is optimized; finally, a new intelligent crack detection method is put forward. The experimental result shows that the system can find all cracks beyond the maximum limit, and effectively identify the type of fracture, and the recognition rate is above 90%. The study provides reference data for engineering detection.

Ultrasonographic patterns in patients with obstructed defaecation.

Science.gov (United States)

Brusciano, L; Limongelli, P; Pescatori, M; Napolitano, V; Gagliardi, G; Maffettone, V; Rossetti, G; del Genio, G; Russo, G; Pizza, F; del Genio, A

2007-08-01

Anal ultrasound is helpful in assessing organic anorectal lesions, but its role in functional disease is still questionable. The purpose of the present study is to assess anal-vaginal-dynamic perineal ultrasonographic findings in patients with obstructed defecation (OD) and healthy controls. Ninety-two consecutive patients (77 women; mean age 51 years; range 21-71) with symptoms of OD were retrospectively evaluated. All patients underwent digital exploration, endoanal and endovaginal ultrasound (US) with rotating probe. Forty-one patients underwent dynamic perineal US with linear probe. Anal manometry and defaecography were performed in 73 and 43 patients, respectively. Ultrasonographic findings of 92 patients with symptoms of OD were compared to 22 healthy controls. Anismus was defined on US when the difference in millimetres between the distance of the inner edge of the puborectalis muscle posteriorly and the probe at rest and on straining was less then 5 mm. Sensitivity and specificity were calculated by assuming defaecography as the gold standard for intussusception and rectocele and proctoscopy for rectal internal mucosal prolapse. Since no gold standard for the diagnosis of anismus was available in the literature, the agreement between anal US and all other diagnostic procedures was evaluated. The incidence of anismus resulted significantly higher (P anismus, anal ultrasonography resulted in agreement with perineal and vaginal US, manometry, defaecography, and digital exam (P < 0.05). Other lesions detected by US in patients with OD include solitary rectal ulcer, rectocele and enterocele. Damage of internal and/or external sphincter was diagnosed at anal US in 19/92 (20%) patients, all continent and with normal manometric values. Anal, vaginal and dynamic perineal ultrasonography can diagnose or confirm many of the abnormalities seen in patients with OD. The value of the information obtained by this non-invasive test and its role in the diagnostic algorithm

Online fouling detection in electrical circulation heaters using neural networks

Energy Technology Data Exchange (ETDEWEB)

Lalot, S. [M.E.T.I.E.R., Longuenesse Cedex (France); Universite de Valenciennes (France). LME; Lecoeuche, S. [M.E.T.I.E.R., Longuenesse Cedex (France); Universite de Lille (France). Laboratoire 13D

2003-06-01

Here is presented a method that is able to detect fouling during the service of a circulation electrical heater. The neural based technique is divided in two major steps: identification and classification. Each step uses a neural network, the connection weights of the first one being the inputs of the second network. Each step is detailed and the main characteristics and abilities of the two neural networks are given. It is shown that the method is able to discriminate fouling from viscosity modification that would lead to the same type of effect on the total heat transfer coefficient. (author)

Fault detection and classification in electrical power transmission system using artificial neural network.

Science.gov (United States)

Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

2015-01-01

This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

An ultrasonographic study of experimental hydronephrosis in rabbit

International Nuclear Information System (INIS)

Choi, Byung Ihn; Yeon, Kyung Mo; Han, Man Chung; Kim, Chu Wan

1984-01-01

Ultrasonographic of rabbit kidney was enformed after induction of simple and infected hydronephrosis to evaluate the sequential sonographic changes in 27 rabbits. Simple hydronephrosis was induced by ligation of the distal ureter and infected hydronephrosis by ligation of the distal ureter and ureteral inoculation of Escherichia coli. Ultrasonography was performed daily during the first week and weekly during the following 5 weeks after induction of simple and infected hydronephrosis. 1. In simple hydronephrosis, the earliest abnormal ultrasonographic finding was splitting of central renal echo complex, which appeared within 1 day after ureteral ligation in all cases. 2. In simple hydronephrosis, complete loss of central renal echo complex and cystic dilatation of pelvis were seen with in 5 days after ureteral ligation in all cases. 3. In infected hydronephrosis, the earliest abnormal ultrasonographic finding was appearance of internal echoes in dependent portion of the pelvis, which appeared within 4 days after inoculation in all cases. 4. In infected hydronephrosis, degree of internal echoes within the pelvis increased progressively with lapse of time and the entire pelvis was filled with internal echoes within 2 weeks after inoculation in all cases. 5. In infected hydronephrosis, echogenecity of internal echoes within the pelvis was similar to that of renal parenchyma in the first week after inoculation, however was weaker than that of renal parenchyma 2 weeks after inoculation in all cases

Ultrasonographic anatomy of bearded dragons (Pogona vitticeps).

Science.gov (United States)

Bucy, Daniel S; Guzman, David Sanchez-Migallon; Zwingenberger, Allison L

2015-04-15

To determine which organs can be reliably visualized ultrasonographically in bearded dragons (Pogona vitticeps), describe their normal ultrasonographic appearance, and describe an ultrasonographic technique for use with this species. Cross-sectional study. 14 healthy bearded dragons (6 females and 8 males). Bearded dragons were manually restrained in dorsal and sternal recumbency, and coelomic organs were evaluated by use of linear 7- to 15-MHz and microconvex 5- to 8-MHz transducers. Visibility, size, echogenicity, and ultrasound transducer position were assessed for each organ. Coelomic ultrasonography with both microconvex and linear ultrasound transducers allowed for visualization of the heart, pleural surface of the lungs, liver, caudal vena cava, aorta, ventral abdominal vein, gallbladder, fat bodies, gastric fundus, cecum, colon, cloaca, kidneys, and testes or ovaries in all animals. The pylorus was visualized in 12 of 14 animals. The small intestinal loops were visualized in 12 of 14 animals with the linear transducer, but could not be reliably identified with the microconvex transducer. The hemipenes were visualized in 7 of 8 males. The adrenal glands and spleen were not identified in any animal. Anechoic free coelomic fluid was present in 11 of 14 animals. Heart width, heart length, ventricular wall thickness, gastric fundus wall thickness, and height of the caudal poles of the kidneys were positively associated with body weight. Testis width was negatively associated with body weight in males. Results indicated coelomic ultrasonography is a potentially valuable imaging modality for assessment of most organs in bearded dragons and can be performed in unsedated animals.

Abdominal ultrasonographic findings at diagnosis of osteosarcoma in dogs and association with treatment outcome.

Science.gov (United States)

Sacornrattana, O; Dervisis, N G; McNiel, E A

2013-09-01

The purpose of this study was to describe abdominal ultrasonographic findings present at diagnosis of osteosarcoma (OSA) in dogs and to investigate for associations with treatment outcome. Medical records from 118 dogs diagnosed with OSA that had abdominal ultrasonography performed as part of their initial evaluation were reviewed. Fifty-seven percent had ultrasonographic abnormalities identified. The organ with the highest frequency of ultrasonographic changes was the spleen. While most sonographic changes were considered to be either benign or of unknown clinical consequences, metastases were identified in three dogs (2.5%), two of which (1.7%) did not have other evidence of metastasis. Dogs with any ultrasonographic abnormality were less likely to receive definitive therapy (P = 0.005) and exhibited shorter survival, although the latter observation was not statistically significant (P = 0.071). However, the identification of lesions in either the liver (P = 0.021) or the kidney (P = 0.003) was statistically associated with shorter survival. © 2012 John Wiley & Sons Ltd.

Molecular Ultrasound Imaging for the Detection of Neural Inflammation

Science.gov (United States)

Volz, Kevin R.

Molecular imaging is a form of nanotechnology that enables the noninvasive examination of biological processes in vivo. Radiopharmaceutical agents are used to selectively target biochemical markers, which permits their detection and evaluation. Early visualization of molecular variations indicative of pathophysiological processes can aid in patient diagnoses and management decisions. Molecular imaging is performed by introducing molecular probes into the body. Molecular probes are often contrast agents that have been nanoengineered to selectively target and tether to molecules, enabling their radiologic identification. Ultrasound contrast agents have been demonstrated as an effective method of detecting perfusion at the tissue level. Through a nanoengineering process, ultrasound contrast agents can be targeted to specific molecules, thereby extending ultrasound's capabilities from the tissue to molecular level. Molecular ultrasound, or targeted contrast enhanced ultrasound (TCEUS), has recently emerged as a popular molecular imaging technique due to its ability to provide real-time anatomical and functional information in the absence of ionizing radiation. However, molecular ultrasound represents a novel form of molecular imaging, and consequently remains largely preclinical. A review of the TCEUS literature revealed multiple preclinical studies demonstrating its success in detecting inflammation in a variety of tissues. Although, a gap was identified in the existing evidence, as TCEUS effectiveness for detection of neural inflammation in the spinal cord was unable to be uncovered. This gap in knowledge, coupled with the profound impacts that this TCEUS application could have clinically, provided rationale for its exploration, and use as contributory evidence for the molecular ultrasound body of literature. An animal model that underwent a contusive spinal cord injury was used to establish preclinical evidence of TCEUS to detect neural inflammation. Imaging was

Neural Network based Minimization of BER in Multi-User Detection in SDMA

OpenAIRE

VENKATA REDDY METTU; KRISHAN KUMAR,; SRIKANTH PULLABHATLA

2011-01-01

In this paper we investigate the use of neural network based minimization of BER in MUD. Neural networks can be used for linear design, Adaptive prediction, Amplitude detection, Character Recognition and many other applications. Adaptive prediction is used in detecting the errors caused in AWGN channel. These errors are rectified by using Widrow-Hoff algorithm by updating their weights andAdaptive prediction methods. Both Widrow-Hoff and Adaptive prediction have been used for rectifying the e...

Ultrasonographic evaluation of normal scapula in the horse

Directory of Open Access Journals (Sweden)

M. S. Ahrari-Khafi

2018-03-01

Full Text Available Scapular fracture is rare in horse, but if happen can cause severe lameness. Due to overlapping of the contralateral scapula and thorax on the scapula, usually radiography is not helpful in its evaluation except in small amount of distal part. This study was intended to document the normal ultrasono-graphic appearance of the equine scapula. Right forelimbs of six horses were used. To facilitate image understanding, a zoning system was developed. Ultrasonography was performed using a 5–11 MHz linear array transducer. Ultrasonographic anatomy of scapula in different parts and planes was imaged and documented. This diagnostic imaging technique revealed a high potential in evaluating scapular surface and possible regional injuries. Ultrasonography could be considered an important addition to radiography in diagnosing fractures in the scapular region.

Development and application of deep convolutional neural network in target detection

Science.gov (United States)

Jiang, Xiaowei; Wang, Chunping; Fu, Qiang

2018-04-01

With the development of big data and algorithms, deep convolution neural networks with more hidden layers have more powerful feature learning and feature expression ability than traditional machine learning methods, making artificial intelligence surpass human level in many fields. This paper first reviews the development and application of deep convolutional neural networks in the field of object detection in recent years, then briefly summarizes and ponders some existing problems in the current research, and the future development of deep convolutional neural network is prospected.

Fault detection and diagnosis using statistical control charts and artificial neural networks

International Nuclear Information System (INIS)

Leger, R.P.; Garland, W.J.; Poehlman, W.F.S.

1995-01-01

In order to operate a successful plant or process, continuous improvement must be made in the areas of safety, quality and reliability. Central to this continuous improvement is the early or proactive detection and correct diagnosis of process faults. This research examines the feasibility of using Cumulative Summation (CUSUM) Control Charts and artificial neural networks together for fault detection and diagnosis (FDD). The proposed FDD strategy was tested on a model of the heat transport system of a CANDU nuclear reactor. The results of the investigation indicate that a FDD system using CUSUM Control Charts and a Radial Basis Function (RBF) neural network is not only feasible but also of promising potential. The control charts and neural network are linked together by using a characteristic fault signature pattern for each fault which is to be detected and diagnosed. When tested, the system was able to eliminate all false alarms at steady state, promptly detect 6 fault conditions and correctly diagnose 5 out of the 6 faults. The diagnosis for the sixth fault was inconclusive. (author). 9 refs., 6 tabs., 7 figs

Image objects detection based on boosting neural network

NARCIS (Netherlands)

Liang, N.; Hegt, J.A.; Mladenov, V.M.

2010-01-01

This paper discusses the problem of object area detection of video frames. The goal is to design a pixel accurate detector for grass, which could be used for object adaptive video enhancement. A boosting neural network is used for creating such a detector. The resulted detector uses both textural

Ultrasonographic abdominal anatomy of healthy captive caracals (Caracal caracal).

Science.gov (United States)

Makungu, Modesta; du Plessis, Wencke M; Barrows, Michelle; Koeppel, Katja N; Groenewald, Hermanus B

2012-09-01

Abdominal ultrasonography was performed in six adult captive caracals (Caracal caracal) to describe the normal abdominal ultrasonographic anatomy. Consistently, the splenic parenchyma was hyperechoic to the liver and kidneys. The relative echogenicity of the right kidney's cortex was inconsistent to the liver. The gall bladder was prominent in five animals and surrounded by a clearly visualized thin, smooth, regular echogenic wall. The wall thickness of the duodenum measured significantly greater compared with that of the jejunum and colon. The duodenum had a significantly thicker mucosal layer compared with that of the stomach. Such knowledge of the normal abdominal ultrasonographic anatomy of individual species is important for accurate diagnosis and interpretation of routine health examinations.

Statistical control chart and neural network classification for improving human fall detection

KAUST Repository

Harrou, Fouzi; Zerrouki, Nabil; Sun, Ying; Houacine, Amrane

2017-01-01

This paper proposes a statistical approach to detect and classify human falls based on both visual data from camera and accelerometric data captured by accelerometer. Specifically, we first use a Shewhart control chart to detect the presence of potential falls by using accelerometric data. Unfortunately, this chart cannot distinguish real falls from fall-like actions, such as lying down. To bypass this difficulty, a neural network classifier is then applied only on the detected cases through visual data. To assess the performance of the proposed method, experiments are conducted on the publicly available fall detection databases: the University of Rzeszow's fall detection (URFD) dataset. Results demonstrate that the detection phase play a key role in reducing the number of sequences used as input into the neural network classifier for classification, significantly reducing computational burden and achieving better accuracy.

Statistical control chart and neural network classification for improving human fall detection

KAUST Repository

Harrou, Fouzi

2017-01-05

This paper proposes a statistical approach to detect and classify human falls based on both visual data from camera and accelerometric data captured by accelerometer. Specifically, we first use a Shewhart control chart to detect the presence of potential falls by using accelerometric data. Unfortunately, this chart cannot distinguish real falls from fall-like actions, such as lying down. To bypass this difficulty, a neural network classifier is then applied only on the detected cases through visual data. To assess the performance of the proposed method, experiments are conducted on the publicly available fall detection databases: the University of Rzeszow\\'s fall detection (URFD) dataset. Results demonstrate that the detection phase play a key role in reducing the number of sequences used as input into the neural network classifier for classification, significantly reducing computational burden and achieving better accuracy.

Ultrasonographic anatomy of reproductive female leopard geckos (Eublepharis macularius).

Science.gov (United States)

Cojean, Ophélie; Vergneau-Grosset, Claire; Masseau, Isabelle

2018-02-19

Captive leopard geckos (Eublepharis macularius) often present to the exotic clinic for gastrointestinal impactions, follicular stasis, or dystocia. To our knowledge, normal ultrasonographic anatomy of these lizards has not been described. The objectives of this prospective, anatomic, analytical study were to develop ultrasound techniques for this species and to describe the normal sonographic anatomy of the head, coelomic cavity, and tail. Eleven, healthy, female leopard geckos were included. A linear array 13-18 MHz transducer was used. Geckos were sedated and restrained in dorsal recumbency for coelomic structure examination and in ventral recumbency for head and tail examinations. Sagittal and transverse images were acquired and authors recorded qualitative and quantitative ultrasonographic characteristics of anatomic structures. The ventral surface of the lungs, liver, gallbladder, caudal vena cava, portal vein, ventral abdominal vein, aorta, ovarian follicles, fat bodies, tail, and brain were visualized in 10 of 11 individuals. In one individual, molt precluded ultrasonographic examination. The heart, kidneys, urinary bladder, spleen, and pancreas were not visualized. The digestive tract was observed in 10 individuals but was too small to be measured. Findings from the current study could be used as a reference for future studies of leopard geckos. © 2018 American College of Veterinary Radiology.

Ultrasonographic Diagnosis and Clinical Evaluation of the Foreign Body Complications in the Compound Stomach of Cattle and Buffaloes

Directory of Open Access Journals (Sweden)

Effat E. El esawy

2015-07-01

Full Text Available This study was aimed to detect and record the clinical and ultrasonographic findings of the different complications resulted from the foreign bodies lodged in the compound stomach of cattle and buffaloes. A total of 105 animals (37 cattle and 68 buffaloes were subjected to study. Based on the clinical and ultrasonographic examination, animals were classified into; acute local reticuloperitonitis (ALRP (15 cattle and 28 buffaloes, chronic local reticuloperitonitis (CLRP (6 cattle and 14 buffaloes, acute diffuse reticuloperitonitis (ADRP (5 cattle and 3buffaloes, reticular abscesses (RA (4 cattle and 7 buffaloes, traumatic pericarditis (TP (6 cattle and16 buffaloes and liver abscess (one cattle. Results revealed that ALRP represented the highest percentage of 40.5% in cattle and 41.2 % in buffalos between the different complications of TRP. TP represented the second complications of higher incidence (16.2% in cows and 23.5% in buffalos. Liver abscess represented the lowest percentage (2.8% and was recorded in cows only. The pregnant animals were affected more than the non pregnant. Clinical findings represented in systemic reaction and pain tests were commonly encountered in TRP and its complications. Some of the affected animals were negatively respond to metal detector test. Results of the present study indicated that the ultrasonographic examination provide a specific echogenic pattern for the different complications of TRP. It was concluded that, clinical examination only is not efficient to give accurate diagnosis of foreign body lodged in the reticulum and rumen and their complications. Ultrasonography is a safe, non invasive diagnostic confirmatory method that could be used for early detection of such conditions.

The value of ultrasonographic examination in the evaluation of liver size and intrahepatic vessels

International Nuclear Information System (INIS)

Narojek, T.

1995-01-01

The objective of the investigations was to comparatively evaluate the size and shape of the liver on the basis of x-ray and ultrasonographic examinations and to estimate the liver parenchyma and intrahepatic vessels on the basis of ultrasonographic examinations. The studies were done on 64 dogs and 13 cats of different breed and sex, aged from 1 day to 18 years. The ultrasonographic examinations were done with the use of Bruel and Kjaer apparatus type 1849 and Concept 2000 by Dynamic Imaging. In 64 animals the ultrasonographic image of the liver was normal. In 7 cases an enlargement of the liver without any changes in the liver parenchyma was noted. An enlargement of intrahepatic veins was found in 8 cases (4 with circulatory insufficiency, 2 with ascites). The assessment of the size and shape of the liver done on that basis of ultrasound examinations agreed with that based on x-ray examinations. The ultrasonic examinations also allowed the evaluation of the liver parenchyma and intrahepatic veins. (author). 9 refs, figs, 3 tabs

T-wave end detection using neural networks and Support Vector Machines.

Science.gov (United States)

Suárez-León, Alexander Alexeis; Varon, Carolina; Willems, Rik; Van Huffel, Sabine; Vázquez-Seisdedos, Carlos Román

2018-05-01

In this paper we propose a new approach for detecting the end of the T-wave in the electrocardiogram (ECG) using Neural Networks and Support Vector Machines. Both, Multilayer Perceptron (MLP) neural networks and Fixed-Size Least-Squares Support Vector Machines (FS-LSSVM) were used as regression algorithms to determine the end of the T-wave. Different strategies for selecting the training set such as random selection, k-means, robust clustering and maximum quadratic (Rényi) entropy were evaluated. Individual parameters were tuned for each method during training and the results are given for the evaluation set. A comparison between MLP and FS-LSSVM approaches was performed. Finally, a fair comparison of the FS-LSSVM method with other state-of-the-art algorithms for detecting the end of the T-wave was included. The experimental results show that FS-LSSVM approaches are more suitable as regression algorithms than MLP neural networks. Despite the small training sets used, the FS-LSSVM methods outperformed the state-of-the-art techniques. FS-LSSVM can be successfully used as a T-wave end detection algorithm in ECG even with small training set sizes. Copyright © 2018 Elsevier Ltd. All rights reserved.

Acoustic Event Detection in Multichannel Audio Using Gated Recurrent Neural Networks with High‐Resolution Spectral Features

Directory of Open Access Journals (Sweden)

Hyoung‐Gook Kim

2017-12-01

Full Text Available Recently, deep recurrent neural networks have achieved great success in various machine learning tasks, and have also been applied for sound event detection. The detection of temporally overlapping sound events in realistic environments is much more challenging than in monophonic detection problems. In this paper, we present an approach to improve the accuracy of polyphonic sound event detection in multichannel audio based on gated recurrent neural networks in combination with auditory spectral features. In the proposed method, human hearing perception‐based spatial and spectral‐domain noise‐reduced harmonic features are extracted from multichannel audio and used as high‐resolution spectral inputs to train gated recurrent neural networks. This provides a fast and stable convergence rate compared to long short‐term memory recurrent neural networks. Our evaluation reveals that the proposed method outperforms the conventional approaches.

Ultrasonographic findings of aspergillus bursitis in a patient with a renal transplantation: a case report

Energy Technology Data Exchange (ETDEWEB)

Kang, Byeong Seong; Yang, Myeon Jun; Kim, Young Min; Youm, Yoon Seok; Choi, Seong Hoon; Park, Sung Bin; Jeong, Ae Kyung [University of Ulsan College of Medicine, Ulsan University Hospital, Ulsan (Korea, Republic of)

2008-04-15

Aspergillus bursitis is an uncommon condition demonstrated as a nonspecific soft tissue mass. To our knowledge, the ultrasonographic findings of aspergillus bursitis in immunocompromised patients have not been previously reported. Here, we report a case of aspergillus bursitis in a renal transplant recipient, accompanied by the associated ultrasonographic findings.

Ultrasonographic findings of aspergillus bursitis in a patient with a renal transplantation: a case report

International Nuclear Information System (INIS)

Kang, Byeong Seong; Yang, Myeon Jun; Kim, Young Min; Youm, Yoon Seok; Choi, Seong Hoon; Park, Sung Bin; Jeong, Ae Kyung

2008-01-01

Aspergillus bursitis is an uncommon condition demonstrated as a nonspecific soft tissue mass. To our knowledge, the ultrasonographic findings of aspergillus bursitis in immunocompromised patients have not been previously reported. Here, we report a case of aspergillus bursitis in a renal transplant recipient, accompanied by the associated ultrasonographic findings

Ultrasonographic evaluation of fibroadenoma in the breast: primary signs of mass

International Nuclear Information System (INIS)

Yoon, Choon Sik; Kim, Mi Hye; Ahn, Chang Soo; Oh, Ki Keun

1994-01-01

To evaluate the ultrasonomammographic findings of breast fibroadenoma. We evaluated the ultrasonographic findings of histopathologically proved 135 fibroadenomas in 103 patients from January 1986 to September 1990, retrospectively. The ultrasonographic examinations were performed with a hand held linear array 5MHz transducer (Acuson 128(USA). Aloka 650, 280(Japan)). Asonopad was also used during the examinations. The common ultrasonographic findings of fibroadenomas usually showed smooth contour in 120 lesions(88.9%), oval or round shape in 114 lesions(84.4%), uniform homogeneous echogenecity in 106 lesions(78.5%), intermediate hypoechoic internal echo pattern in 105 lesions(77.8%), thin boundary echo in 117 lesions(86.7%), lateral shadowings in 97 lesions(72%), and posterior acoustic enhancement in 56 lesions(41%). The longitudinal/transverse ratio of fibroadenoma was revealed between 0.2 and 1.14 (mean 0.58) and usually under 1.0 (68.9%). Finally, most of fibroadenomas are easily diagnosed by ultrasonography but if differential diagnosis from malignant breast mass is difficult due to atypical appearance, other combined modalities such as filmmammography, fine needle aspiration biopsy and MRI are necessary

Ultrasonographic evaluation of fibroadenoma in the breast: primary signs of mass

Energy Technology Data Exchange (ETDEWEB)

Yoon, Choon Sik; Kim, Mi Hye; Ahn, Chang Soo; Oh, Ki Keun [College of Medicine, Yonsei University, Seoul (Korea, Republic of)

1994-01-15

To evaluate the ultrasonomammographic findings of breast fibroadenoma. We evaluated the ultrasonographic findings of histopathologically proved 135 fibroadenomas in 103 patients from January 1986 to September 1990, retrospectively. The ultrasonographic examinations were performed with a hand held linear array 5MHz transducer (Acuson 128(USA). Aloka 650, 280(Japan)). Asonopad was also used during the examinations. The common ultrasonographic findings of fibroadenomas usually showed smooth contour in 120 lesions(88.9%), oval or round shape in 114 lesions(84.4%), uniform homogeneous echogenecity in 106 lesions(78.5%), intermediate hypoechoic internal echo pattern in 105 lesions(77.8%), thin boundary echo in 117 lesions(86.7%), lateral shadowings in 97 lesions(72%), and posterior acoustic enhancement in 56 lesions(41%). The longitudinal/transverse ratio of fibroadenoma was revealed between 0.2 and 1.14 (mean 0.58) and usually under 1.0 (68.9%). Finally, most of fibroadenomas are easily diagnosed by ultrasonography but if differential diagnosis from malignant breast mass is difficult due to atypical appearance, other combined modalities such as filmmammography, fine needle aspiration biopsy and MRI are necessary.

Clinical and ultrasonographic implications of uterine leiomyomatosis in pregnancy.

Science.gov (United States)

Piazze Garnica, J; Gallo, G; Marzano, P F; Vozzi, G; Mazzocco, M; Anceschi, M M; Rolfini, G

1995-01-01

To study the complications related to leiomyomatosis in pregnancy by clinical and ultrasonographic assessment. A retrospective study. All pregnancies admitted to the 2nd Institute of Gynecology and Obstetrics, Policlinico Umberto I, in the period between January 1992 to December 1993 were surveyed. Gestational age at the time of ultrasonographic neoplasm diagnosis was 25.1 +/- 13.4 weeks, 'we found no correlation between maternal age or parity affecting pregnancy outcome, Leiomyomatosis complicated pregnancy rate was 1.68%. Myomatosis was diagnosed clinically in 25 of 67 cases (37.3%). Regarding the location of the neoplasm, 59% was located in the corpus-uteri, 21% was considered a diffuse neoplasm and the 14% was located in the fundus. Threatened abortion was the most frequent complication (20%), abortion was the second (16.4%). We observed an increased abortion threat rate (p pregnancies complicated by myomatosis, and the indication for surgery was given either primarily or exclusively by the presence of myomatous formation in 19 cases (50%). Our study suggests that location of the leiomyoma in relation to the placenta is a higher risk factor than its size, and that there is a higher risk for threats of abortion and abortion rates in pregnancies complicated by leiomyomatosis. We recommend that every pregnant woman with a suspected myoma should be ultrasonographically scanned.

Ultrasonographic determination of fetal gender

International Nuclear Information System (INIS)

Kim, Il Young; Kim, Dae Ho; Lee, Byung Ho; Bae, Dong Han

1985-01-01

Sonographic determination of fetal gender was attempted prospectively in most pregnancies of more than 26 weeks. We studied 193 cases of pregnancies with ultrasound for recent 9 months from June 1984 to February 1985 at department of radiology, Soonchunhyang university, Soonchunhyang Chunan hospital, and analysed ultrasonographic finding of fetal gender. The results were as follows; 1. Overall accuracy rate for fetal gender is 90%. 2. Accuracy rate for male fetus is 97.8%. 3. Accuracy rate for female fetus is 88.2%

Ultrasonographic findings 6 months after 11-gauge vacuum-assisted large-core breast biopsy

Energy Technology Data Exchange (ETDEWEB)

Docktor, B.J.L.; MacGregor, J.H.; Burrowes, P.W. [Foothills Medical Centre, Dept. of Diagnostic Imaging, Calgary, Alberta (Canada)]. E-mail: bobbie.docktor@calgaryhealthregion.ca

2004-06-01

To assess the ultrasonographic features of post-biopsy change 6 months after 11-gauge vacuum-assisted large-core breast biopsy of pathologically proven benign lesions. Using the literature as a reference, we hypothesized that large-core breast biopsy would result in tissue changes that may mimic malignancy and may be more apparent on ultrasonography than on mammography. Two radiologists whose subspecialty is breast imaging retrospectively reviewed the pre-biopsy and 6-month follow-up sonograms of 24 patients with pathologically proven benign lesions. The images were assessed for the number and type of ultrasonographic features. A Breast Imaging Reporting and Data System (BI-RADS) category was assigned to each lesion before biopsy and at 6-month follow-up. The composition of breast tissue surrounding the lesion was assessed as fatty, mixed fibroglandular or dense. The frequency of ultrasonographic changes at 6 months after 11-gauge vacuum-assisted large-core breast biopsy was more frequent than the rate of post-biopsy change previously reported to occur mammographically. The nature of these changes may mimic malignancy in some cases. The ultrasonographic appearance of the breast after large-core breast biopsy may mimic malignancy and is, therefore, a potential pitfall when interpreting a post-biopsy sonogram. (author)

Ultrasonographic findings 6 months after 11-gauge vacuum-assisted large-core breast biopsy.

Science.gov (United States)

Docktor, Bobbie Jo L; MacGregor, John Henry; Burrowes, Paul W

2004-06-01

To assess the ultrasonographic features of post-biopsy change 6 months after 11-gauge vacuum-assisted large-core breast biopsy of pathologically proven benign lesions. Using the literature as a reference, we hypothesized that large-core breast biopsy would result in tissue changes that may mimic malignancy and may be more apparent on ultrasonography than on mammography. Two radiologists whose subspecialty is breast imaging retrospectively reviewed the pre-biopsy and 6-month follow-up sonograms of 24 patients with pathologically proven benign lesions. The images were assessed for the number and type of ultrasonographic features. A Breast Imaging Reporting and Data System (BI-RADS) category was assigned to each lesion before biopsy and at 6-month follow-up. The composition of breast tissue surrounding the lesion was assessed as fatty, mixed fibroglandular or dense. The frequency of ultrasonographic changes at 6 months after 11-gauge vacuum-assisted large-core breast biopsy was more frequent than the rate of post-biopsy change previously reported to occur mammographically. The nature of these changes may mimic malignancy in some cases. The ultrasonographic appearance of the breast after large-core breast biopsy may mimic malignancy and is, therefore, a potential pitfall when interpreting a post-biopsy sonogram.

Ultrasonographic findings 6 months after 11-gauge vacuum-assisted large-core breast biopsy

International Nuclear Information System (INIS)

Docktor, B.J.L.; MacGregor, J.H.; Burrowes, P.W.

2004-01-01

To assess the ultrasonographic features of post-biopsy change 6 months after 11-gauge vacuum-assisted large-core breast biopsy of pathologically proven benign lesions. Using the literature as a reference, we hypothesized that large-core breast biopsy would result in tissue changes that may mimic malignancy and may be more apparent on ultrasonography than on mammography. Two radiologists whose subspecialty is breast imaging retrospectively reviewed the pre-biopsy and 6-month follow-up sonograms of 24 patients with pathologically proven benign lesions. The images were assessed for the number and type of ultrasonographic features. A Breast Imaging Reporting and Data System (BI-RADS) category was assigned to each lesion before biopsy and at 6-month follow-up. The composition of breast tissue surrounding the lesion was assessed as fatty, mixed fibroglandular or dense. The frequency of ultrasonographic changes at 6 months after 11-gauge vacuum-assisted large-core breast biopsy was more frequent than the rate of post-biopsy change previously reported to occur mammographically. The nature of these changes may mimic malignancy in some cases. The ultrasonographic appearance of the breast after large-core breast biopsy may mimic malignancy and is, therefore, a potential pitfall when interpreting a post-biopsy sonogram. (author)

The biopsy of the boar testes using ultrasonographic examination

Directory of Open Access Journals (Sweden)

Laima Liepa

2014-03-01

Full Text Available The biopsy of live animal testes is an important clinical manipulation to control spermatogenesis and reproductive system pathologies. The aim was to develop a method of boar testes biopsy using a biopsy gun with ultrasound guidance and to investigate the influence of this procedure on the boar testes parenchyma and quality of ejaculate. The biopsy was carried out in six 8-month-old boars. Fourteen days prior to and 21 days after biopsy, the quality of ejaculate was examined (weight of ejaculate; concentration and motility of spermatozoa with a seven-day intervals. Ultrasound images of the testes parenchyma were recorded three times: directly before and 15 minutes after the biopsy, then 21 days after the procedure. The testes biopsies of generally anesthetized boars were performed with the biopsy gun for needle biopsy with a 12cm long, disposable 16-gauge needle 1.8mm in diameter (Vitesse through 1cm skin incision in the depth of 1.2-1.6cm of parenchyma. Fifteen minutes after the biopsy, macroscopic injures of the parenchyma of all the boar testes were not detected in the ultrasound image. Twenty one days after biopsy, the hyperechogenic line 0.1-0.2cm in diameter was seen in the testes parenchyma of six boars in the depth of 1.2-1.6cm. The biopsy of boar testes did not influence the quality of boars ejaculate. The ultrasonographic examination of boar testicles before the biopsy reduced possibilities to traumatize large blood vessels of the testes. A perfect boar testicular biopsy was easy to perform using ultrasonographic examination in the pigsty conditions.

COMPARISON OF ULTRASONOGRAPHIC PLACENTA EXAMINATION WITH PATHOHISTOLOGIC VERIFICATION OF FETAL ANOMALIES

Directory of Open Access Journals (Sweden)

Dragan Loncar

2007-04-01

Full Text Available Ultrasonographic diagnostics is a sovereign diagnostic method of discovering disorders in growth and development of embryo. The main aim of this research is The Comparison of Ultrasonographic Placenta Examination with Pathohistologic Treatment of Placenta considering those pregnancies previously verified to have embryo anomalies and which were ended by the procedure of feticide. During the period of 2001 – 2004, 15 pregnant women, with gestation between the 24th and 28th week, were hospitalized in our clinic. Ultrasonographic placenta examination was carried out during the expertise sonography immediately before deciding to commit feticide. The descriptive medical findings were divided into the clinical entities estimating the continuity of basal body, insertion, volume, and echo-structure of placenta substance. The procedure of feticide was carried out in regular treatments using intracardial application of 7,4 % KCl or transabdominal, intra-amnial instillation of 20 % NaCl under the control of ultrasound.The patients with the embryo anomalies were divided into three groups:I – the group with the diagnosis of embryo hydrocephalusII – the group with the diagnosis of other anomalies of growth of embryo's CNSIII – the group of patients with other embryo anomaliesPathohistologic placenta examinations were carried out in the Department of Pathology and Forensic Medicine in CC Kragujevac.The ultrasonographic placenta finding of the patients with the different embryo anomalies was not statistically very different (x2 – test; p=0,073. However, besides the lack of significant difference, what is reasonable considering the size of the sample, we notice quite different ultrasonographic findings of the placenta examination of the patients having the embryo with hydrocephalus in comparison to those patients having the other embryo anomalies of CNS. The ultrasonographic placenta examination of the patients having the other embryo anomalies was

Analysis of ultrasonographic findings in liver cirrhosis

Energy Technology Data Exchange (ETDEWEB)

Jeong, Seong Wook; Suh, Won Hyuck [Korea University College of Medicine, Seoul (Korea, Republic of)

1988-10-15

The association of liver cirrhosis with high amplitude echoes is well recognized. In Korea, despite the common occurrence of liver cirrhosis, little has been written regarding its ultrasonographic features. Retrospective evaluation of abdominal sonograms in 122 patients with liver cirrhosis was made using Weill's classification. The results were as followings: 1. 122 cases consist of 23 cases with Type I, 37 cases with Type II, 33 cases with Type IIIa, 28 cases with Type IIIb, and 1 case with Type IV. 2. Neither clinical finding nor laboratory data discriminates remarkable difference between each type. 3. Liver size is not in direct proportion to ultrasonographic type although hepatic retraction was more frequent in Type III, IV than in Type I and II. 4. Ancillary findings such as splenomegaly, portal hypertension and ascites are seen in Type I, II as frequently as in Type III, IV. Therefore, these different patterns are considered to be related to morphological types rather than phases. 5. Area of diverse echogenicity was revealed as malignant transformation in cirrhotic liver by RI scan by cold area.

Ultrasonographic evaluation of the healing of ventral midline abdominal incisions in the horse.

Science.gov (United States)

Wilson, D A; Badertscher, R R; Boero, M J; Baker, G J; Foreman, J H

1989-06-01

Ultrasonography was used to evaluate the ventral midline incisions of 21 ponies following exploratory laparotomy. The incisions were evaluated before surgery and at weekly intervals from one to seven weeks after surgery. Both 5.0 and 7.5 MHz linear array and 7.5 MHz sector transducers were used for the evaluations. The incisional complications observed were drainage, oedema, suture sinus formation, suture abscess, superficial dehiscence and incisional hernia. Ultrasonographic imaging of the ventral midline incision was an easy, reliable and objective method for detecting and monitoring the progression of incisional complications in a non-invasive manner.

Ultrasonographic evaluation of Hashimoto's thyroiditis: Comparison of size and echo change with thyroid function

International Nuclear Information System (INIS)

Lee, Kang Rae; Cho, Jae Hyun; Kim, Yun Jeong; Kim, Hyun Man; Park, Rae Woong; Suh, Jung Ho; Kang, Byung Chul

1999-01-01

To demonstrate sonographic features of Hashimoto's thyroiditis according to the thyroid function. We reviewed 54 thyroid ultrasonographic examinations of untreated Hashimoto's thyroiditis. We reviewed thyroid ultrasonographic examinations and focused on the presence of ill-defined low echoic lesions and glandular enlargement. We performed another thyroid ultrasonographic examination of 14 healthy volunteers, in order to obtain normal size of thyroid gland. Comparison was made between these morphologic characteristics and functional stage of the disease. The mean diameter of thyroid gland was 2.16 ± 0.43 cm in patients with Hashimoto's thyroiditis, and 1.41 ± 0.42 cm in normal control group of the thyroid gland. There was no statistically significant relationship between thyroid function and size. There was morphologic abnormalities in 46 patients (85%). Among them, 7 patients revealed diffuse low echogenicity in the entire thyroid gland, 32 patients showed peripherally located, ill-defined focal hypoechoic lesion, and 7 patients showed solitary or multiple. well-defined nodular lesions. Decreased echogenicity of the thyroid gland was related to hypothyroid status. Hashimoto's thyroiditis has specific morphologue characteristics in ultrasonographic features, which are well correlated with thyroid function.

Pancreatic pseudocysts. Radiological and ultrasonographic studies

Energy Technology Data Exchange (ETDEWEB)

Contrera, J.D.; Uemura, L.; Palma, J.K.; Souza, L.P. de; Ferraz, L.R.L.; Magalhaes, P.J.A. (Sao Paulo Univ., Ribeirao Preto (Brazil). Faculdade de Medicina)

Radiological and ultrasonographic studies of ten patients with surgically confirmed pancreatic pseudocysts were reviewed. All of them were male, with previous story of chronic alcoholism and clinical evidences of pancreatitis. The most important radiological finding consisted of a mass opacifying the epigastrium, displacing the stomach and bowel loops. ultrasound studies showed that the lesions were predominantly cystic, rounded or oval-shaped with smooth or irregular contours and of various sizes.

Comparison between ultrasonographic and clinical findings in 43 dogs with gallbladder mucoceles.

Science.gov (United States)

Choi, Jihye; Kim, Ahyoung; Keh, Seoyeon; Oh, Juyeon; Kim, Hyunwook; Yoon, Junghee

2014-01-01

Cholecystectomy is the current standard recommended treatment for dogs with gallbladder mucoceles. However, medical management with monitoring has also been recommended for asymptomatic dogs. The purpose of this retrospective study was to compare ultrasonographic patterns of gallbladder mucoceles with clinical disease status in a group of dogs. For each included dog, the ultrasonographic pattern of the mucocele was classified into one of six types: type 1, immobile echogenic bile; type 2, incomplete stellate pattern; type 3, typical stellate pattern; type 4, kiwi like pattern and stellate combination; type 5, kiwi like pattern with residual central echogenic bile; and type 6, kiwi like pattern. A total of 43 dogs were included. Twenty-four dogs, including 11 dogs with gallbladder rupture, were symptomatic. Nineteen dogs were asymptomatic. Cholecystectomy (n = 19), medical therapy (n = 17), or monitoring (n = 6) treatments were applied according to clinical signs and owners' requests. One dog suspected of having gallbladder rupture was euthanized. Frequencies of gallbladder mucocele patterns were as follows: type 1 = 10 (23%), type 2 = 13 (30%), type 3 = 5 (12%), type 4 = 11 (26%), type 5 = 4 (9%), and type 6 = 0. In dogs with gallbladder rupture, type 2 (8/13) was the most common. No significant correlations were found between ultrasonographic patterns of gallbladder mucoceles and clinical disease status or gallbladder rupture. Findings indicated that ultrasonographic patterns of gallbladder mucoceles may not be valid bases for treatment recommendations in dogs. © 2013 American College of Veterinary Radiology.

Musculoskeletal ultrasonographic evaluation of lower limb enthesopathy in ankylosing spondylitis and Behçet’s disease: Relation to clinical status and disease activity

Directory of Open Access Journals (Sweden)

E A Baraka

2016-01-01

Conclusion Ultrasonographic changes at the entheseal sites of the lower limbs are prevalent in both AS and BD. These changes are more frequently related to functional and articular involvement. MSUS is more sensitive than clinical examination in detecting enthesopathies of the lower limbs in both AS and BD patients.

Detecting modulated signals in modulated noise: (II) neural thresholds in the songbird forebrain.

Science.gov (United States)

Bee, Mark A; Buschermöhle, Michael; Klump, Georg M

2007-10-01

Sounds in the real world fluctuate in amplitude. The vertebrate auditory system exploits patterns of amplitude fluctuations to improve signal detection in noise. One experimental paradigm demonstrating these general effects has been used in psychophysical studies of 'comodulation detection difference' (CDD). The CDD effect refers to the fact that thresholds for detecting a modulated, narrowband noise signal are lower when the envelopes of flanking bands of modulated noise are comodulated with each other, but fluctuate independently of the signal compared with conditions in which the envelopes of the signal and flanking bands are all comodulated. Here, we report results from a study of the neural correlates of CDD in European starlings (Sturnus vulgaris). We manipulated: (i) the envelope correlations between a narrowband noise signal and a masker comprised of six flanking bands of noise; (ii) the signal onset delay relative to masker onset; (iii) signal duration; and (iv) masker spectrum level. Masked detection thresholds were determined from neural responses using signal detection theory. Across conditions, the magnitude of neural CDD ranged between 2 and 8 dB, which is similar to that reported in a companion psychophysical study of starlings [U. Langemann & G.M. Klump (2007) Eur. J. Neurosci., 26, 1969-1978]. We found little evidence to suggest that neural CDD resulted from the across-channel processing of auditory grouping cues related to common envelope fluctuations and synchronous onsets between the signal and flanking bands. We discuss a within-channel model of peripheral processing that explains many of our results.

Aspects of Ultrasonographic Diagnostics of Pregnancy in Bitches depending on the first mating

Directory of Open Access Journals (Sweden)

Aissi

Full Text Available The aim of the present study was to follow up the potential of routine ultrasonographic diagnostics of pregnancy in bitches depending on the first mating. The experiments were performed on 32 bitches. Pregnancy was detected using transabdominal ultrasonography with Siemens sonoline adara equipment and a covex 5 MHz probe. Subsequent serial examinations were made to sonographically characterize normal canine prenatal development based about the first mating. An enlarged uterus, gestational sacs and fetal poles were recognized as the features of early bitchs pregnancy and were first seen at 16 and 21 days, respectively. Cardiac activity was detected earliest on gestational day 22 and recognizable canine fetal morphology appeared at day 28. Generalized fetal movements were first noted at day 28. [Veterinary World 2008; 1(10.000: 293-295

Optimizing a neural network for detection of moving vehicles in video

Science.gov (United States)

Fischer, Noëlle M.; Kruithof, Maarten C.; Bouma, Henri

2017-10-01

In the field of security and defense, it is extremely important to reliably detect moving objects, such as cars, ships, drones and missiles. Detection and analysis of moving objects in cameras near borders could be helpful to reduce illicit trading, drug trafficking, irregular border crossing, trafficking in human beings and smuggling. Many recent benchmarks have shown that convolutional neural networks are performing well in the detection of objects in images. Most deep-learning research effort focuses on classification or detection on single images. However, the detection of dynamic changes (e.g., moving objects, actions and events) in streaming video is extremely relevant for surveillance and forensic applications. In this paper, we combine an end-to-end feedforward neural network for static detection with a recurrent Long Short-Term Memory (LSTM) network for multi-frame analysis. We present a practical guide with special attention to the selection of the optimizer and batch size. The end-to-end network is able to localize and recognize the vehicles in video from traffic cameras. We show an efficient way to collect relevant in-domain data for training with minimal manual labor. Our results show that the combination with LSTM improves performance for the detection of moving vehicles.

Ultrasonographic Characteristics of Subacute Granulomatous Thyroiditis

International Nuclear Information System (INIS)

Park, Sun Young; Kim, Eun Kyung; Kim, Min Jung; Oh, Ki Keun; Hong, Soon Won; Park, Cheong Soo; Kim, Byung Moon

2006-01-01

We wanted to describe the characteristic ultrasonography (US) features and clinical findings for making the diagnosis of subacute granulomatous thyroiditis. A total of 31 lesions from 27 patients were confirmed as subacute granulomatous thyroiditis by US-guided fine needle aspiration biopsy. We analyzed the ultrasonographic findings such as the lesion's size, margin and shape, the discrepancy between length and breadth and the vascularity. The clinical findings such as acute neck pain or fever were reviewed. The follow-up clinical and ultrasonographic data were reviewed for 15 patients. The thyroid gland was found to be enlarged in five patients, it was normal size in 20 patients and it was smaller in two patients. All the lesions had focally ill-defined hypoechogenicity. Hypervascularity was not noted in any of the lesions. Painful neck swelling was present in 18 patients. An accompanying fever was documented in nine of the 18 patients. Twelve patients showed disappearance (n = 3) or a decreased size (n = 9) of their lesions on follow-up US. The presence of ill-defined hypoechoic thyroid lesions without a discrete round or oval shape is characteristic for subacute granulomatous thyroiditis, and particularly when this is associated with painful neck swelling and/or fever

Ultrasonographic Characteristics of Subacute Granulomatous Thyroiditis

Energy Technology Data Exchange (ETDEWEB)

Park, Sun Young [Gachon University Gil Medical Center, Incheon (Korea, Republic of); Kim, Eun Kyung; Kim, Min Jung; Oh, Ki Keun; Hong, Soon Won; Park, Cheong Soo [Yonsei University College of Medicine, Seoul (Korea, Republic of); Kim, Byung Moon [Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of)

2006-12-15

We wanted to describe the characteristic ultrasonography (US) features and clinical findings for making the diagnosis of subacute granulomatous thyroiditis. A total of 31 lesions from 27 patients were confirmed as subacute granulomatous thyroiditis by US-guided fine needle aspiration biopsy. We analyzed the ultrasonographic findings such as the lesion's size, margin and shape, the discrepancy between length and breadth and the vascularity. The clinical findings such as acute neck pain or fever were reviewed. The follow-up clinical and ultrasonographic data were reviewed for 15 patients. The thyroid gland was found to be enlarged in five patients, it was normal size in 20 patients and it was smaller in two patients. All the lesions had focally ill-defined hypoechogenicity. Hypervascularity was not noted in any of the lesions. Painful neck swelling was present in 18 patients. An accompanying fever was documented in nine of the 18 patients. Twelve patients showed disappearance (n = 3) or a decreased size (n = 9) of their lesions on follow-up US. The presence of ill-defined hypoechoic thyroid lesions without a discrete round or oval shape is characteristic for subacute granulomatous thyroiditis, and particularly when this is associated with painful neck swelling and/or fever.

Multiscale Convolutional Neural Networks for Hand Detection

Directory of Open Access Journals (Sweden)

Shiyang Yan

2017-01-01

Full Text Available Unconstrained hand detection in still images plays an important role in many hand-related vision problems, for example, hand tracking, gesture analysis, human action recognition and human-machine interaction, and sign language recognition. Although hand detection has been extensively studied for decades, it is still a challenging task with many problems to be tackled. The contributing factors for this complexity include heavy occlusion, low resolution, varying illumination conditions, different hand gestures, and the complex interactions between hands and objects or other hands. In this paper, we propose a multiscale deep learning model for unconstrained hand detection in still images. Deep learning models, and deep convolutional neural networks (CNNs in particular, have achieved state-of-the-art performances in many vision benchmarks. Developed from the region-based CNN (R-CNN model, we propose a hand detection scheme based on candidate regions generated by a generic region proposal algorithm, followed by multiscale information fusion from the popular VGG16 model. Two benchmark datasets were applied to validate the proposed method, namely, the Oxford Hand Detection Dataset and the VIVA Hand Detection Challenge. We achieved state-of-the-art results on the Oxford Hand Detection Dataset and had satisfactory performance in the VIVA Hand Detection Challenge.

Wavelet-based higher-order neural networks for mine detection in thermal IR imagery

Science.gov (United States)

Baertlein, Brian A.; Liao, Wen-Jiao

2000-08-01

An image processing technique is described for the detection of miens in RI imagery. The proposed technique is based on a third-order neural network, which processes the output of a wavelet packet transform. The technique is inherently invariant to changes in signature position, rotation and scaling. The well-known memory limitations that arise with higher-order neural networks are addressed by (1) the data compression capabilities of wavelet packets, (2) protections of the image data into a space of similar triangles, and (3) quantization of that 'triangle space'. Using these techniques, image chips of size 28 by 28, which would require 0(109) neural net weights, are processed by a network having 0(102) weights. ROC curves are presented for mine detection in real and simulated imagery.

Robust fault detection of wind energy conversion systems based on dynamic neural networks.

Science.gov (United States)

Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

2014-01-01

Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

Ultrasonographic extended-view technique for evaluation of abdominal fat distribution in lean women with polycystic ovary syndrome.

Science.gov (United States)

Battaglia, Cesare; Battaglia, Bruno; Mancini, Fulvia; Paradisi, Roberto; Fabbri, Raffaella; Venturoli, Stefano

2011-06-01

To estimate whether, by using a new ultrasonographic technique (extended view; XTD view), young lean women with polycystic ovary syndrome (PCOS) have a more android fat distribution in comparison with normally menstruating women with ultrasonographic evidence of polycystic ovaries (PCO) and healthy control subjects, matched for both age and body mass index. Prospective observational study. University Hospital. Forty-nine lean women with PCOS, 42 eumenorrheic women with bilateral PCO and 40 healthy volunteers with regular ovulatory cycles. Fasting blood sampling, ultrasonographic and Doppler analyses and blood pressure monitoring. Medical examination, biochemical and hormonal parameters, ultrasonographic abdominal fat measurements, ultrasonographic evaluation of carotid intima-media thickness and Doppler analysis of ophthalmic artery. An oral glucose tolerance test was performed to analyze glucose, insulin and C-peptide levels. The XTD ultrasonographic preperitoneal area was significantly larger in women with PCOS than in control subjects (p=0.011). The preperitoneal/subcutaneous ratio was significantly higher in women with PCOS (1.1±0.26) compared with women with PCO (0.84±0.13; p=0.05) and control women (0.67±0.13; pPCOS women (1.93±0.57) than in control subjects (1.84±0.38; p=0.041). Total cholesterol, triglycerides and LDL cholesterol were significantly higher in women with PCOS than in those with PCO and in control subjects. Women with PCOS have an android fat pattern correlated with an age-dependent increased risk of cardiovascular diseases. © 2011 The Authors Acta Obstetricia et Gynecologica Scandinavica© 2011 Nordic Federation of Societies of Obstetrics and Gynecology.

Robust recurrent neural network modeling for software fault detection and correction prediction

International Nuclear Information System (INIS)

Hu, Q.P.; Xie, M.; Ng, S.H.; Levitin, G.

2007-01-01

Software fault detection and correction processes are related although different, and they should be studied together. A practical approach is to apply software reliability growth models to model fault detection, and fault correction process is assumed to be a delayed process. On the other hand, the artificial neural networks model, as a data-driven approach, tries to model these two processes together with no assumptions. Specifically, feedforward backpropagation networks have shown their advantages over analytical models in fault number predictions. In this paper, the following approach is explored. First, recurrent neural networks are applied to model these two processes together. Within this framework, a systematic networks configuration approach is developed with genetic algorithm according to the prediction performance. In order to provide robust predictions, an extra factor characterizing the dispersion of prediction repetitions is incorporated into the performance function. Comparisons with feedforward neural networks and analytical models are developed with respect to a real data set

A robust neural network-based approach for microseismic event detection

KAUST Repository

Akram, Jubran; Ovcharenko, Oleg; Peter, Daniel

2017-01-01

We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset

Acoustic leak detection at complicated topologies using neural netwoks

International Nuclear Information System (INIS)

Hessel, G.; Schmitt, W.; Weiss, F.P.

1994-01-01

Considering the shortcomings of all the existing leak detecting principles, a new method again based on the measurement of the leak induced sound but also applying pattern recognition is being developed. The capability of neural networks to localize leaks at the reactor pressure vessel (RPV) head of VVER-440 reactors is discussed. (orig./DG)

Ultrasonographic characteristics of the abdominal esophagus and cardia in dogs.

Science.gov (United States)

Gory, Guillaume; Rault, Delphine N; Gatel, Laure; Dally, Claire; Belli, Patrick; Couturier, Laurent; Cauvin, Eddy

2014-01-01

Differential diagnoses for regurgitation and vomiting in dogs include diseases of the gastroesophageal junction. The purpose of this cross-sectional study was to describe ultrasonographic characteristics of the abdominal esophagus and gastric cardia in normal dogs and dogs with clinical disease involving this region. A total of 126 dogs with no clinical signs of gastrointestinal disease and six dogs with clinical diseases involving the gastroesophageal junction were included. For seven euthanized dogs, ultrasonographic features were also compared with gross pathology and histopathology. Cardial and abdominal esophageal wall thicknesses were measured ultrasonographically for all normal dogs and effects of weight, sex, age, and stomach filling were tested. Five layers could be identified in normal esophageal and cardial walls. The inner esophageal layer was echogenic, corresponding to the cornified mucosa and glandular portion of the submucosa. The cardia was characterized by a thick muscularis, and a transitional zone between echogenic esophageal and hypoechoic gastric mucosal layers. Mean (±SD) cardial wall thicknesses for normal dogs were 7.6 mm (±1.6), 9.7 mm (±1.8), 10.8 mm (±1.6), 13.3 mm (±2.5) for dogs in the dog weight group. Ultrasonography assisted diagnosis in all six clinically affected dogs. Findings supported the use of transabdominal ultrasonography as a diagnostic test for dogs with suspected gastroesophageal disease. © 2014 American College of Veterinary Radiology.

Ultrasonographic Detection of Tooth Flaws

Science.gov (United States)

Bertoncini, C. A.; Hinders, M. K.; Ghorayeb, S. R.

2010-02-01

The goal of our work is to adapt pulse-echo ultrasound into a high resolution imaging modality for early detection of oral diseases and for monitoring treatment outcome. In this talk we discuss our preliminary results in the detection of: demineralization of the enamel and dentin, demineralization or caries under and around existing restorations, caries on occlusal and interproximal surfaces, cracks of enamel and dentin, calculus, and periapical lesions. In vitro immersion tank experiments are compared to results from a handpiece which uses a compliant delay line to couple the ultrasound to the tooth surface. Because the waveform echoes are complex, and in order to make clinical interpretation of ultrasonic waveform data in real time, it is necessary to automatically interpret the signals. We apply the dynamic wavelet fingerprint algorithms to identify and delineate echographic features that correspond to the flaws of interest in teeth. The resulting features show a clear distinction between flawed and unflawed waveforms collected with an ultrasonic handpiece on both phantom and human cadaver teeth.

Convolutional neural networks for event-related potential detection: impact of the architecture.

Science.gov (United States)

Cecotti, H

2017-07-01

The detection of brain responses at the single-trial level in the electroencephalogram (EEG) such as event-related potentials (ERPs) is a difficult problem that requires different processing steps to extract relevant discriminant features. While most of the signal and classification techniques for the detection of brain responses are based on linear algebra, different pattern recognition techniques such as convolutional neural network (CNN), as a type of deep learning technique, have shown some interests as they are able to process the signal after limited pre-processing. In this study, we propose to investigate the performance of CNNs in relation of their architecture and in relation to how they are evaluated: a single system for each subject, or a system for all the subjects. More particularly, we want to address the change of performance that can be observed between specifying a neural network to a subject, or by considering a neural network for a group of subjects, taking advantage of a larger number of trials from different subjects. The results support the conclusion that a convolutional neural network trained on different subjects can lead to an AUC above 0.9 by using an appropriate architecture using spatial filtering and shift invariant layers.

Clinical and ultrasonographic study of patients presenting with transvaginal mesh complications.

Science.gov (United States)

Manonai, Jittima; Rostaminia, Ghazaleh; Denson, Lindsay; Shobeiri, S Abbas

2016-03-01

The objective of this study was to investigate the clinical and ultrasonographic findings of women who had three-dimensional endovaginal ultrasound (EVUS) for the management of vaginal mesh complications. This was a retrospective study of patients that had EVUS due to mesh complications at a tertiary care center. The clinical charts were reviewed. The stored 3D volumes were reviewed regarding mesh information by two examiners independently. The predictive value of physical examination for detection of vaginal mesh was calculated. Patient outcomes were reviewed. Seventy-nine patients presented to our center because of their, or their physicians' concern regarding mesh complications. Forty-one (51.9%) had vaginal/pelvic pain, and 51/62 (82.2%) of sexually active women experienced dyspareunia. According to ultrasonographic findings, mesh or sling was not demonstrated in six patients who believed they have had mesh/sling implantation. The positive predictive value for vaginal examination was 94.5% (95% CI: 84.9%-98.8%), negative predictive value was 12.5% (95% CI: 2.8%-32.4%), sensitivity was 72.2% (95% CI: 59.4%-81.2%), and specificity was 50.0% (95% CI: 12.4%-87.6%). Fifty-four patients were indicated for surgical treatment. Median postoperative review was 12 (range, 3-18) months and 38/53 (71.7%) patients were satisfied. The most common complaints of vaginal mesh complications were pain and dyspareunia. EVUS appeared to be helpful for assessing mesh presence, location, and extent including planning for surgical intervention. © 2015 Wiley Periodicals, Inc.

Ultrasonographic Imaging of Normal and Impacted Omasum in Indian Crossbred Cows

Directory of Open Access Journals (Sweden)

Sheikh Imran

2011-01-01

Full Text Available Omasal impaction is a serious disease problem in cattle in India, but it is difficult to diagnose clinically. Ultrasonography has been proposed for the noninvasive evaluation of omasal disease. The objectives of this study were to compare the in vitro and in vivo ultrasonographic appearance of the omasum and to compare omasal appearance, limits, and size in clinically healthy cows with those in cows having confirmed omasal impaction. A 3.5 MHz curvilinear transducer was used to image and record the appearance of the omasum in vitro in a water bath, and its appearance, dorsal and ventral limits, and size in 10 healthy Indian Jersey/Red Sindhi crossbred cows. The results were compared with the ultrasonographic data collected from 5 cows with omasal impaction, as confirmed at necropsy. On moving the transducer dorsoventrally in each intercostal space and below the costal arch, the wall of omasum could be seen as an echogenic arc-like structure. The difference between mean dorsoventral extents of the normal and impacted omasums was statistically insignificant. These results suggest that ultrasonographic imaging may not be useful in the diagnosis of omasal impaction in Indian crossbred cows, however, additional studies may be warranted.

Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks.

Science.gov (United States)

Zhong, Jiandan; Lei, Tao; Yao, Guangle

2017-11-24

Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed.

Ultrasonographic findings of breast disease

International Nuclear Information System (INIS)

Choi, Kwang Uk; Kim, Sung Eun; Lee, Hee Chung; Shin, Kyung Ja; Kim, Young Chul; Lee, Sang Chun

1989-01-01

The authors analyzed ultrasonographic findings of 60 cases of breast lesions which were proven surgically of pathologically at Seoul Red Cross Hospital from September 1986 to February 1989. The results were as follows; 1. There were 30 fibrocystic diseases, 12 fibroadenomas, 8 carcinomas, 3 abscesses, 3 foreign bodies, 2 gynecomastias, 1 intraductal papilloma, 1 malignant cystosarcoma phylloides. 2. Ultrasonography provided accurate information for the size, location, internal structure and relationship between lesion and adjacent structure. 3. Ultrasonography can be used as an adjunct to film mammography in selective patients and useful for guiding fine needle aspiration biopsies

Ultrasonographic findings of breast disease

Energy Technology Data Exchange (ETDEWEB)

Choi, Kwang Uk; Kim, Sung Eun; Lee, Hee Chung; Shin, Kyung Ja; Kim, Young Chul; Lee, Sang Chun [Seoul Red Cross Hopital, Seoul (Korea, Republic of)

1989-10-15

The authors analyzed ultrasonographic findings of 60 cases of breast lesions which were proven surgically of pathologically at Seoul Red Cross Hospital from September 1986 to February 1989. The results were as follows; 1. There were 30 fibrocystic diseases, 12 fibroadenomas, 8 carcinomas, 3 abscesses, 3 foreign bodies, 2 gynecomastias, 1 intraductal papilloma, 1 malignant cystosarcoma phylloides. 2. Ultrasonography provided accurate information for the size, location, internal structure and relationship between lesion and adjacent structure. 3. Ultrasonography can be used as an adjunct to film mammography in selective patients and useful for guiding fine needle aspiration biopsies.

Ultrasonographic findings of tuberculous peritonitis

Energy Technology Data Exchange (ETDEWEB)

Lee, Dong Ho; Oh, C. H.; Koh, Y. T.; Lim, J. H. [Seoul National University College of Medicine, Seoul (Korea, Republic of)

1989-12-15

Sonograms in forty two patients with tuberculous peritonitis of the wet-ascitic type were retrospectively analyzed. The ascites was clear in 24 patients (57%). There were septations, membranes and debris in 6 (14%), floating debris in 5 (12%), mobile strands or membranes in 4 (10%), and fixed septations in three(7%). Other findings were increased hepatic echogenicity, hepatosplenomegaly, pleural effusion, omental cake, thickened mesentery with adherent bowel loops, lymphadenopathy, thickening of the ileal wall, presented in order of frequency. The ultrasonographic findings are not specific for tuberculous peritonitis, but may give profitable information and protect the patient from unnecessary laparotomy

An optimal ultrasonographic diagnostic test for early gout: A prospective controlled study.

Science.gov (United States)

Norkuviene, Eleonora; Petraitis, Mykolas; Apanaviciene, Indre; Virviciute, Dalia; Baranauskaite, Asta

2017-08-01

Objective To identify the optimal sites for classification of early gout by ultrasonography. Methods Sixty patients with monosodium urate crystal-proven gout (25 with early gout [≤2-year symptom duration], 35 with late gout [>2-year symptom duration], and 36 normouricemic healthy controls) from one centre were prospectively evaluated. Standardized blinded ultrasound examination of 36 joints and the triceps and patellar tendons was performed to identify tophi and the double contour (DC) sign. Results Ultrasonographic sensitivity was lower in early than late gout. Binary logistic regression analysis showed that two ultrasonographic signs (tophi in the first metatarsophalangeal joint [odds ratio, 16.46] and the DC sign in the ankle [odds ratio, 25.18]) significantly contributed to the final model for early gout diagnosis (sensitivity and specificity of 84% and 81%, respectively). The inter-reader reliability kappa value for the DC sign and tophi was 0.712. Conclusions Four-joint investigation (both first metatarsophalangeal joints for tophi and both ankles for the DC sign) is feasible and reliable and could be proposed as a screening test for early ultrasonographic gout classification in daily practice.

Deep convolutional neural networks for detection of rail surface defects

NARCIS (Netherlands)

Faghih Roohi, S.; Hajizadeh, S.; Nunez Vicencio, Alfredo; Babuska, R.; De Schutter, B.H.K.; Estevez, Pablo A.; Angelov, Plamen P.; Del Moral Hernandez, Emilio

2016-01-01

In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and

Deep Recurrent Neural Networks for seizure detection and early seizure detection systems

Energy Technology Data Exchange (ETDEWEB)

Talathi, S. S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2017-06-05

Epilepsy is common neurological diseases, affecting about 0.6-0.8 % of world population. Epileptic patients suffer from chronic unprovoked seizures, which can result in broad spectrum of debilitating medical and social consequences. Since seizures, in general, occur infrequently and are unpredictable, automated seizure detection systems are recommended to screen for seizures during long-term electroencephalogram (EEG) recordings. In addition, systems for early seizure detection can lead to the development of new types of intervention systems that are designed to control or shorten the duration of seizure events. In this article, we investigate the utility of recurrent neural networks (RNNs) in designing seizure detection and early seizure detection systems. We propose a deep learning framework via the use of Gated Recurrent Unit (GRU) RNNs for seizure detection. We use publicly available data in order to evaluate our method and demonstrate very promising evaluation results with overall accuracy close to 100 %. We also systematically investigate the application of our method for early seizure warning systems. Our method can detect about 98% of seizure events within the first 5 seconds of the overall epileptic seizure duration.

Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security.

Directory of Open Access Journals (Sweden)

Min-Joo Kang

Full Text Available A novel intrusion detection system (IDS using a deep neural network (DNN is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN, therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN bus.

Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security.

Science.gov (United States)

Kang, Min-Joo; Kang, Je-Won

2016-01-01

A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus.

Detecting phase transitions in a neural network and its application to classification of syndromes in traditional Chinese medicine

Energy Technology Data Exchange (ETDEWEB)

Chen, J; Xi, G [Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, 100080, Beijing (China); Wang, W [Beijing University of Chinese Medicine, 100029, Beijing (China)], E-mail: guangcheng.xi@ia.ac.cn

2008-02-15

Detecting phase transitions in neural networks (determined or random) presents a challenging subject for phase transitions play a key role in human brain activity. In this paper, we detect numerically phase transitions in two types of random neural network(RNN) under proper parameters.

Usefulness of ultrasonographic evaluation in primary and secondary hyperparathyroidism

International Nuclear Information System (INIS)

Jeon, Tae Joo; Kim, Eun Kyung; Lee, Jong Doo; Park, Jung Soo; Lee, Jong Tae; Yoo, Hyung Sik

1999-01-01

To evaluate the accuracy and ultrasonographic findings of primary and secondary hyperparathyroidism (HPT) and correlate them with pathologic results. We reviewed 31 cases of surgically confirmed primary (n=22) and secondary (n=9) hyperparathyroidism. We used 10 or 7.5 MHz linear transducer and reviewed the location, contour, size and echogenicity of lesions. Then we evaluated the detection rate of parathyroid lesions based on surgical result and compared the result of 99m Tc-sestamibi scan (15 cases). Location of primary HPT was left lower in 9, left upper in 5, right lower in 4, right upper in 3, left midportion in 1 and superior mediastinum in 1. Lesions showed variable echogenicity-mild low echo (2), moderate low echo (10), severe low echo (2), isoecho (4) and heterogeneous echo pattern (1). All the lesions except 5 were well defined and 3 lesions had echogenic rim. Posterior enhancement and lateral shadowing were noted in 3 and 4 lesions, respectively. Nineteen of 23 primary lesions were detected by ultrasonography (82.6%) and well correlated with sestamibi scan. In case of secondary HPT, most were well defined low echoic nodular lesions, and we could detect 6 of 9 patients (67%) and 15 of 36 lesions (41.7%). Only 6 of 24 secondary lesion were detected by sestamibi scan (25%). The detection rate of ultrasonography in primary HPT was fairly good and well correlated with the result of the 99m Tc-sestamibi scan, but both diagnostic modalities were not promising in secondary HPT.

Ultrasonographic findings of septic arthritis and osteomyelitis in neonatal hip

International Nuclear Information System (INIS)

Lee, Seung Hoon; Jung, Kun Sik; Koh, Jung Kon; Im, Myung Ah; Kwon, Kwi Ryun; Kim, Sung Soo

2000-01-01

To evaluate ultrasonographic findings of neonatal patients who confirmed and treated as hip joint septic arthritis and osteomyelitis. We retrospectively examined clinical feature and radiologic findings of 7 neonatal patients ranging from 8 to 28 days of age who were examined from January 1966 to December 1998 at nursery and were confirmed and treated on the diagnosis of septic arthritis and osteomyelitis. Clinical features of the patients were comparatively analyzed with radiologic findings including plain radiographs, ultrasonography, bone scan and MRI. We emphasized importance of ultrasonographic findings of these patients. Ultrasonography was performed first of all in all cases after the symptom onset. Other examinations were performed on the same day or a few days later after ultrasonography. Ultrasonography revealed abnormal finding in 85.7% (6/7) of all cases. Plain radiographs revealed abnormal findings in 28.6% (2/7). Bone scan revealed decreased uptake in 66.7%(2/3). MRI revealed abnormal signal intensity in 100%(3/3). Ultrasonographic findings of the patients were deep soft swelling in 85.7% (6/7) of all cases, periosteal elevation in 57.1% (4/7), synovial thickening in 42.8% (3/7), synovial effusion in 42.8%(3/7), echogenic debris or clot in 28.5% (2/7), cortical erosion in 28.5% (2/7), and subperiosteal abscess in 14.2% (1/7). Ultrasonography is a useful modality to diagnose septic arthritis and osteomyelitis in neonatal hip.

A study on the cholecystolcholagiographic and ultrasonographic findings of biliary disease

International Nuclear Information System (INIS)

Shin, Kyoung Ja; Bang, Dae Hong; Lee, Sang Chun; Kim, Jae Seop

1983-01-01

In the 88 cases of biliary disease, which was proven in Seoul Red Cross Hospital from Jan, 1980 to Dec. 1981, comparative studies were made with oral and IV cholecystocholangiographic findings and ultrasonographic findings. The results were: 1. In the 18 cases of GB stones, there are 17 cases (94.4%) of positive findings in cholecysto-cholangiography with detection of stone in 7 cases (38.9%), while in sonographic study, 16 cases (88.9%) are shown positive findings with detection of stone in 11 cases (61.1%). 2. In the 17 cases of acalculous cholecystitis, the diagnostic accuracy is 88.2% in cholecystocholangiography and 64.7% in sonography. 3. In the 7 cases of CBD stones, all cases are shown positive findings in cholecystocholangiography with detection of stone in only one case (14.3%), while 6 cases (85.7%) of positive findings are shown in sonography with detection of stone in all cases. 4. I.V. cholangiography is more accurate diagnostic procedure rather than oral GB study in the cases of poor or non-functioning GB. 5. Sonography is the choice of procedure in the diagnosis of stones, while in the cases of cholecystitis, cholecystocholangiography is more useful diagnostic procedure

Ultrasonographic Features of Papillary Thyroid Carcinoma in Patients with Graves' Disease

Science.gov (United States)

Chung, Jin Ook; Cho, Dong Hyeok; Chung, Dong Jin

2010-01-01

Background/Aims To characterize ultrasonographic findings in papillary thyroid carcinoma (PTC) combined with Graves' disease. Methods Medical records and ultrasonographic findings of 1,013 patients with Graves' disease and 3,380 patients without Graves' disease were analyzed retrospectively. A diagnosis of PTC was based on a pathologic examination. Results The frequency of hypoechogenicity was lower in patients with PTC and Graves' disease than in patients with PTC alone (p Graves' disease was significantly higher than in those with PTC alone (p Graves' disease was characterized by more ill-defined borders and less frequency of overall calcification, punctate calcification, and heterogeneous echogenicity, although the difference was not statistically significant. Conclusions Our results suggest that patients with Graves' disease more frequently have atypical PTC findings on ultrasonography. PMID:20195406

On the robustness of EC-PC spike detection method for online neural recording.

Science.gov (United States)

Zhou, Yin; Wu, Tong; Rastegarnia, Amir; Guan, Cuntai; Keefer, Edward; Yang, Zhi

2014-09-30

Online spike detection is an important step to compress neural data and perform real-time neural information decoding. An unsupervised, automatic, yet robust signal processing is strongly desired, thus it can support a wide range of applications. We have developed a novel spike detection algorithm called "exponential component-polynomial component" (EC-PC) spike detection. We firstly evaluate the robustness of the EC-PC spike detector under different firing rates and SNRs. Secondly, we show that the detection Precision can be quantitatively derived without requiring additional user input parameters. We have realized the algorithm (including training) into a 0.13 μm CMOS chip, where an unsupervised, nonparametric operation has been demonstrated. Both simulated data and real data are used to evaluate the method under different firing rates (FRs), SNRs. The results show that the EC-PC spike detector is the most robust in comparison with some popular detectors. Moreover, the EC-PC detector can track changes in the background noise due to the ability to re-estimate the neural data distribution. Both real and synthesized data have been used for testing the proposed algorithm in comparison with other methods, including the absolute thresholding detector (AT), median absolute deviation detector (MAD), nonlinear energy operator detector (NEO), and continuous wavelet detector (CWD). Comparative testing results reveals that the EP-PC detection algorithm performs better than the other algorithms regardless of recording conditions. The EC-PC spike detector can be considered as an unsupervised and robust online spike detection. It is also suitable for hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

Ultrasonographic evaluation of Hashimoto's thyroiditis: Comparison of size and echo change with thyroid function

Energy Technology Data Exchange (ETDEWEB)

Lee, Kang Rae; Cho, Jae Hyun; Kim, Yun Jeong; Kim, Hyun Man; Park, Rae Woong; Suh, Jung Ho [Aju University School of Medicine, Suwon (Korea, Republic of); Kang, Byung Chul [Ewha Womans University College of Medicine, Seoul (Korea, Republic of)

1999-12-15

To demonstrate sonographic features of Hashimoto's thyroiditis according to the thyroid function. We reviewed 54 thyroid ultrasonographic examinations of untreated Hashimoto's thyroiditis. We reviewed thyroid ultrasonographic examinations and focused on the presence of ill-defined low echoic lesions and glandular enlargement. We performed another thyroid ultrasonographic examination of 14 healthy volunteers, in order to obtain normal size of thyroid gland. Comparison was made between these morphologic characteristics and functional stage of the disease. The mean diameter of thyroid gland was 2.16 {+-} 0.43 cm in patients with Hashimoto's thyroiditis, and 1.41 {+-} 0.42 cm in normal control group of the thyroid gland. There was no statistically significant relationship between thyroid function and size. There was morphologic abnormalities in 46 patients (85%). Among them, 7 patients revealed diffuse low echogenicity in the entire thyroid gland, 32 patients showed peripherally located, ill-defined focal hypoechoic lesion, and 7 patients showed solitary or multiple. well-defined nodular lesions. Decreased echogenicity of the thyroid gland was related to hypothyroid status. Hashimoto's thyroiditis has specific morphologue characteristics in ultrasonographic features, which are well correlated with thyroid function.

Neural Network Based Intrusion Detection System for Critical Infrastructures

Energy Technology Data Exchange (ETDEWEB)

Todd Vollmer; Ondrej Linda; Milos Manic

2009-07-01

Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

Ultrasonographic assessment of renal length in 310 Turkish children ...

African Journals Online (AJOL)

Scanning was performed with a 3.5 MHz ultrasound probe in the supine position. The ultrasonographic appearance of the kidneys we measured was normal. The maximum length of each kidney was measured. The renal length was correlated with somatic parameters including age, body height and weight. Regression ...

Performance of an artificial neural network for vertical root fracture detection: an ex vivo study.

Science.gov (United States)

Kositbowornchai, Suwadee; Plermkamon, Supattra; Tangkosol, Tawan

2013-04-01

To develop an artificial neural network for vertical root fracture detection. A probabilistic neural network design was used to clarify whether a tooth root was sound or had a vertical root fracture. Two hundred images (50 sound and 150 vertical root fractures) derived from digital radiography--used to train and test the artificial neural network--were divided into three groups according to the number of training and test data sets: 80/120,105/95 and 130/70, respectively. Either training or tested data were evaluated using grey-scale data per line passing through the root. These data were normalized to reduce the grey-scale variance and fed as input data of the neural network. The variance of function in recognition data was calculated between 0 and 1 to select the best performance of neural network. The performance of the neural network was evaluated using a diagnostic test. After testing data under several variances of function, we found the highest sensitivity (98%), specificity (90.5%) and accuracy (95.7%) occurred in Group three, for which the variance of function in recognition data was between 0.025 and 0.005. The neural network designed in this study has sufficient sensitivity, specificity and accuracy to be a model for vertical root fracture detection. © 2012 John Wiley & Sons A/S.

Mitral valve prolapse associated with celiac artery stenosis: a new ultrasonographic syndrome?

Directory of Open Access Journals (Sweden)

Arcari Luciano

2004-12-01

Full Text Available Abstract Background Celiac artery stenosis (CAS may be caused by atherosclerotic degeneration or compression exerted by the arched ligament of the diaphragm. Mitral valve prolapse (MVP is the most common valvular disorder. There are no reports on an association between CAS and MVP. Methods 1560 (41% out of 3780 consecutive patients undergoing echocardiographic assessment of MVP, had Doppler sonography of the celiac tract to detect CAS. Results CAS was found in 57 (3.7% subjects (23 males and 34 females none of whom complained of symptoms related to visceral ischemia. MVP was observed in 47 (82.4% subjects with and 118 (7.9% without CAS (p Conclusion CAS and MVP seem to be significantly associated in patients undergoing consecutive ultrasonographic screening.

Cellular Neural Network-Based Methods for Distributed Network Intrusion Detection

Directory of Open Access Journals (Sweden)

Kang Xie

2015-01-01

Full Text Available According to the problems of current distributed architecture intrusion detection systems (DIDS, a new online distributed intrusion detection model based on cellular neural network (CNN was proposed, in which discrete-time CNN (DTCNN was used as weak classifier in each local node and state-controlled CNN (SCCNN was used as global detection method, respectively. We further proposed a new method for design template parameters of SCCNN via solving Linear Matrix Inequality. Experimental results based on KDD CUP 99 dataset show its feasibility and effectiveness. Emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI implementation which allows the distributed intrusion detection to be performed better.

Wind Turbine Fault Detection based on Artificial Neural Network Analysis of SCADA Data

DEFF Research Database (Denmark)

Herp, Jürgen; S. Nadimi, Esmaeil

2015-01-01

Slowly developing faults in wind turbine can, when not detected and fixed on time, cause severe damage and downtime. We are proposing a fault detection method based on Artificial Neural Networks (ANN) and the recordings from Supervisory Control and Data Acquisition (SCADA) systems installed in wind...

Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees

International Nuclear Information System (INIS)

Jerebko, Anna K.; Summers, Ronald M.; Malley, James D.; Franaszek, Marek; Johnson, C. Daniel

2003-01-01

Detection of colonic polyps in CT colonography is problematic due to complexities of polyp shape and the surface of the normal colon. Published results indicate the feasibility of computer-aided detection of polyps but better classifiers are needed to improve specificity. In this paper we compare the classification results of two approaches: neural networks and recursive binary trees. As our starting point we collect surface geometry information from three-dimensional reconstruction of the colon, followed by a filter based on selected variables such as region density, Gaussian and average curvature and sphericity. The filter returns sites that are candidate polyps, based on earlier work using detection thresholds, to which the neural nets or the binary trees are applied. A data set of 39 polyps from 3 to 25 mm in size was used in our investigation. For both neural net and binary trees we use tenfold cross-validation to better estimate the true error rates. The backpropagation neural net with one hidden layer trained with Levenberg-Marquardt algorithm achieved the best results: sensitivity 90% and specificity 95% with 16 false positives per study

Failure detection studies by layered neural network

International Nuclear Information System (INIS)

Ciftcioglu, O.; Seker, S.; Turkcan, E.

1991-06-01

Failure detection studies by layered neural network (NN) are described. The particular application area is an operating nuclear power plant and the failure detection is of concern as result of system surveillance in real-time. The NN system is considered to be consisting of 3 layers, one of which being hidden, and the NN parameters are determined adaptively by the backpropagation (BP) method, the process being the training phase. Studies are performed using the power spectra of the pressure signal of the primary system of an operating nuclear power plant of PWR type. The studies revealed that, by means of NN approach, failure detection can effectively be carried out using the redundant information as well as this is the case in this work; namely, from measurement of the primary pressure signals one can estimate the primary system coolant temperature and hence the deviation from the operational temperature state, the operational status identified in the training phase being referred to as normal. (author). 13 refs.; 4 figs.; 2 tabs

The Ultrasonographic Findings of Bifid Median Nerve

International Nuclear Information System (INIS)

Park, Hee Jin; Park, Noh Hyuck; Joh, Joon Hee; Lee, Sung Moon

2009-01-01

We wanted to evaluate the ultrasonographic findings of bifid median nerve and its clinical significance. We retrospectively reviewed five cases (three men and two women, mean age: 54 years) of incidentally found bifid median nerve from 264 cases of clinically suspected carpal-tunnel syndrome that were seen at our hospital during last 6 years. Doppler sonography was performed in all five cases and MR angiography was done in one case for detecting a persistent median artery. The difference (ΔCSA) between the sum of the cross-sectional areas of the bifid median nerve at the pisiform level (CSA2) and the cross-sectional area proximal to the bifurcation(CSA1) was calculated. The incidence of a bifid median nerve was 1.9%. All the patients presented with a tingling sensation on a hand and two patients had nocturnal pain. All the cases showed bifurcation of the nerve bundle proximal to the carpal tunnel. The margins appeared relatively smooth and each bundle showed a characteristic fascicular pattern. A persistent median artery was noted between the bundles in four cases. ΔCSA was more than 2 mm 2 in four cases. Bifid median nerve with a persistent median artery is a relatively rare normal variance and these are very important findings before performing surgical intervention to avoid potential nerve injury and massive bleeding. We highly suggest that radiologists should understand the anatomical characteristics of this anomaly and make efforts to detect it

A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks.

Science.gov (United States)

Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun

2016-10-13

The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.

Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection

Directory of Open Access Journals (Sweden)

Erik Marchi

2017-01-01

Full Text Available In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. Only recent studies introduced (pseudo-generative models for acoustic novelty detection with recurrent neural networks in the form of an autoencoder. In these approaches, auditory spectral features of the next short term frame are predicted from the previous frames by means of Long-Short Term Memory recurrent denoising autoencoders. The reconstruction error between the input and the output of the autoencoder is used as activation signal to detect novel events. There is no evidence of studies focused on comparing previous efforts to automatically recognize novel events from audio signals and giving a broad and in depth evaluation of recurrent neural network-based autoencoders. The present contribution aims to consistently evaluate our recent novel approaches to fill this white spot in the literature and provide insight by extensive evaluations carried out on three databases: A3Novelty, PASCAL CHiME, and PROMETHEUS. Besides providing an extensive analysis of novel and state-of-the-art methods, the article shows how RNN-based autoencoders outperform statistical approaches up to an absolute improvement of 16.4% average F-measure over the three databases.

Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection.

Science.gov (United States)

Marchi, Erik; Vesperini, Fabio; Squartini, Stefano; Schuller, Björn

2017-01-01

In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. Only recent studies introduced (pseudo-)generative models for acoustic novelty detection with recurrent neural networks in the form of an autoencoder. In these approaches, auditory spectral features of the next short term frame are predicted from the previous frames by means of Long-Short Term Memory recurrent denoising autoencoders. The reconstruction error between the input and the output of the autoencoder is used as activation signal to detect novel events. There is no evidence of studies focused on comparing previous efforts to automatically recognize novel events from audio signals and giving a broad and in depth evaluation of recurrent neural network-based autoencoders. The present contribution aims to consistently evaluate our recent novel approaches to fill this white spot in the literature and provide insight by extensive evaluations carried out on three databases: A3Novelty, PASCAL CHiME, and PROMETHEUS. Besides providing an extensive analysis of novel and state-of-the-art methods, the article shows how RNN-based autoencoders outperform statistical approaches up to an absolute improvement of 16.4% average F -measure over the three databases.

Importance of ultrasonographic study in the diagnosis of the neoplasia of pancreas

International Nuclear Information System (INIS)

Salomon, M.; Rodriguez, Z.; Diaz, J.A.; Fong, A.

1988-01-01

Thirty clinical histories of patients discharged from hospital, with diagnosis of neoplasia of the pancreas, were reviewed. The patients were assisted at the ''saturnino Lora'' Teaching Provincial Hospital, Santiago de Cuba, from January 1983 to June 1985. Remarkable effectiveness of abdominal ultrasonographic study for the diagnosis of this affection was demonstrated at its correlation with other complementary examinations, such as radiographic and laparoscopic examinations, besides surgical findings and verification with histopathologic diagnosis. The tumor was more frequently located in the head of the pancreas and echogenicity and dilation of biliary and choledocus ducts were its main ultrasonographic characteristics. Adenocarcinoma was the prevailing histologic type. These findings agree with those related in the reviewed literature

The use of ultrasonographic techniques for the diagnosis of retinopathy of prematurity

International Nuclear Information System (INIS)

Modzejewska, M.

2006-01-01

Introduction: Ultrasonographic techniques are commonly used for the imaging of various tissue structures and organs, including the eyeball and orbit. The non-invasive ultrasound imaging is safe for the patient and may be repeated after a short time unlike in the case of other radiological techniques. In pediatric ophthalmology, ultrasonography plays a major role as an auxiliary examination for the diagnosis of various intraocular diseases, pathologies of the retina and choroid, and retrobulbar conditions. Ultrasonography is of major importance in diagnosing eye disorders associated with opacity preventing visual inspection of the posterior eye segment. Methods: Among ultrasonographic techniques in pediatric ophthalmology the most frequently used are B-scan, A-scan, and Doppler ultrasonography. Because of the resolution of ultrasonographic methods in comparison to radiological techniques, they play an important role in monitoring the dynamics of pathological processes in retinopathy of prematurity (ROP). Other radiological methods such as CT, MRI or subtractive angiography do not offer a detailed view of retinal attachment or vitreo-retinal proliferation. Conclusion: Ultrasonography as an auxiliary examination at subsequent stages of ROP helps to document the changes and in case of corneal opacity connected with progression of vitreo-retinal abnormalities is the basis for the diagnosis. (author)

Abnormality Detection in Mammography using Deep Convolutional Neural Networks

OpenAIRE

Xi, Pengcheng; Shu, Chang; Goubran, Rafik

2018-01-01

Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be tra...

Detection of Oil Chestnuts Infected by Blue Mold Using Near-Infrared Hyperspectral Imaging Combined with Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Lei Feng

2018-06-01

Full Text Available Mildew damage is a major reason for chestnut poor quality and yield loss. In this study, a near-infrared hyperspectral imaging system in the 874–1734 nm spectral range was applied to detect the mildew damage to chestnuts caused by blue mold. Principal component analysis (PCA scored images were firstly employed to qualitatively and intuitively distinguish moldy chestnuts from healthy chestnuts. Spectral data were extracted from the hyperspectral images. A successive projections algorithm (SPA was used to select 12 optimal wavelengths. Artificial neural networks, including back propagation neural network (BPNN, evolutionary neural network (ENN, extreme learning machine (ELM, general regression neural network (GRNN and radial basis neural network (RBNN were used to build models using the full spectra and optimal wavelengths to distinguish moldy chestnuts. BPNN and ENN models using full spectra and optimal wavelengths obtained satisfactory performances, with classification accuracies all surpassing 99%. The results indicate the potential for the rapid and non-destructive detection of moldy chestnuts by hyperspectral imaging, which would help to develop online detection system for healthy and blue mold infected chestnuts.

Ultrasonographic examination in chest disease

Energy Technology Data Exchange (ETDEWEB)

Choe, K.O.; Lee, J.D.; Yoo, H.S. [Yonsei University College of Medicine, Seoul (Korea, Republic of)

1983-12-15

Ultrasonographic examination is not widely applied to chest disease, but is may give useful information when the acoustic window for a lesion exist. We did perform ultrasound examination in 68 cases of chest disease. 1. The cases of pleural diseases was predominant; pleural effusion 35 cases, pleural metastatic tumor 2 case, mesothelioma 2 cases and fibrous thickening 1 case, total 40 cases. It was useful to differentiate pleural effusion and fibrous thickening or parenchymal lesion simulating pleural disease, to localize the optimal aspiration site for a loculated empyema, to detect pleural bumorhidden by effusion such as metastatic tumor or mesothelioma. 2. 15 cases of parenchymal lesion and 2 cases of extra pleural mass was examined. The echo pattern of consolidation and atelectasis shows typical multiple tubular streaks within the echogenic area. The echogenicity of the peripheral mass due to primary bronchogenic carcinoma, parenchymal or extrapleural metastatic tumor and granuloma were compared. 3. In the cases of pleural or parenchymal cystic lesions, such as loculated empyema or lung abscess, because of strong reverberation artifact from posterior border of the lesion, the prediction of cystic and solid lesion is sometimes difficult. 4. In 7 cases of mediastinal lesion, cystic lesion show free echo and posterior enhancement. In contrast, solid or fat component show characteristic echo pattern. 5. In the cases of juxta diaphragmatic lesion, sonogram can confirm the underlying intraabdominal pathology, in this case subphrenic abscess

Ultrasonographic examination in chest disease

International Nuclear Information System (INIS)

Choe, K.O.; Lee, J.D.; Yoo, H.S.

1983-01-01

Ultrasonographic examination is not widely applied to chest disease, but is may give useful information when the acoustic window for a lesion exist. We did perform ultrasound examination in 68 cases of chest disease. 1. The cases of pleural diseases was predominant; pleural effusion 35 cases, pleural metastatic tumor 2 case, mesothelioma 2 cases and fibrous thickening 1 case, total 40 cases. It was useful to differentiate pleural effusion and fibrous thickening or parenchymal lesion simulating pleural disease, to localize the optimal aspiration site for a loculated empyema, to detect pleural bumorhidden by effusion such as metastatic tumor or mesothelioma. 2. 15 cases of parenchymal lesion and 2 cases of extra pleural mass was examined. The echo pattern of consolidation and atelectasis shows typical multiple tubular streaks within the echogenic area. The echogenicity of the peripheral mass due to primary bronchogenic carcinoma, parenchymal or extrapleural metastatic tumor and granuloma were compared. 3. In the cases of pleural or parenchymal cystic lesions, such as loculated empyema or lung abscess, because of strong reverberation artifact from posterior border of the lesion, the prediction of cystic and solid lesion is sometimes difficult. 4. In 7 cases of mediastinal lesion, cystic lesion show free echo and posterior enhancement. In contrast, solid or fat component show characteristic echo pattern. 5. In the cases of juxta diaphragmatic lesion, sonogram can confirm the underlying intraabdominal pathology, in this case subphrenic abscess

Anomalous Signal Detection in ELF Band Electromagnetic Wave using Multi-layer Neural Network with Wavelet Decomposition

Science.gov (United States)

Itai, Akitoshi; Yasukawa, Hiroshi; Takumi, Ichi; Hata, Masayasu

It is well known that electromagnetic waves radiated from the earth's crust are useful for predicting earthquakes. We analyze the electromagnetic waves received at the extremely low frequency band of 223Hz. These observed signals contain the seismic radiation from the earth's crust, but also include several undesired signals. Our research focuses on the signal detection technique to identify an anomalous signal corresponding to the seismic radiation in the observed signal. Conventional anomalous signal detections lack a wide applicability due to their assumptions, e.g. the digital data have to be observed at the same time or the same sensor. In order to overcome the limitation related to the observed signal, we proposed the anomalous signals detection based on a multi-layer neural network which is trained by digital data observed during a span of a day. In the neural network approach, training data do not need to be recorded at the same place or the same time. However, some noises, which have a large amplitude, are detected as the anomalous signal. This paper develops a multi-layer neural network to decrease the false detection of the anomalous signal from the electromagnetic wave. The training data for the proposed network is the decomposed signal of the observed signal during several days, since the seismic radiations are often recorded from several days to a couple of weeks. Results show that the proposed neural network is useful to achieve the accurate detection of the anomalous signal that indicates seismic activity.

The value of ultrasonographic examinations in the diagnosis of focal changes in the hepatic parenchyma

International Nuclear Information System (INIS)

Narojek, T.

1995-01-01

The aim of the investigation was the comparison of the diagnostic value of the clinical, radiological and ultrasonographic examinations in the diagnosis of focal changes in the liver and the determination of the relations between the changes in the ultrasound image of the liver and the changes in other organs. The investigation was performed on 24 animals: 20 dogs and 4 cats of different breeds and sex, aged 1.5 to 14 years. The ultrasonographic examinations were done using the apparatus of Bruel and Kjaer type 1849 and Concept 2000 of Dynamic Imaging. The following changes were diagnosed in the ultrasonic picture: echogenic changes in 5 animals, hypoechogenic in 2 animals, normechogenic in 2 animals, hyperechogenic in 8 animals and changes of mixed echogenicity in 5 animals. The connection between clinical signs and the results of X-ray and ultrasonographic examination allowed the recognition of the changes in the liver as cysts, abscess and neoplasm of the liver. (author). 9 refs, 8 figs, 5 tabs

A fast button surface defects detection method based on convolutional neural network

Science.gov (United States)

Liu, Lizhe; Cao, Danhua; Wu, Songlin; Wu, Yubin; Wei, Taoran

2018-01-01

Considering the complexity of the button surface texture and the variety of buttons and defects, we propose a fast visual method for button surface defect detection, based on convolutional neural network (CNN). CNN has the ability to extract the essential features by training, avoiding designing complex feature operators adapted to different kinds of buttons, textures and defects. Firstly, we obtain the normalized button region and then use HOG-SVM method to identify the front and back side of the button. Finally, a convolutional neural network is developed to recognize the defects. Aiming at detecting the subtle defects, we propose a network structure with multiple feature channels input. To deal with the defects of different scales, we take a strategy of multi-scale image block detection. The experimental results show that our method is valid for a variety of buttons and able to recognize all kinds of defects that have occurred, including dent, crack, stain, hole, wrong paint and uneven. The detection rate exceeds 96%, which is much better than traditional methods based on SVM and methods based on template match. Our method can reach the speed of 5 fps on DSP based smart camera with 600 MHz frequency.

Abdominal ultrasonographic manifestation of Henoch-schonlein purpura

International Nuclear Information System (INIS)

Eun, Hyo Won; Kim, Mi Sung; Kang, Beoung Chul; Lee, Sun Wha

1998-01-01

The purpose of this study was to describe the ultrasonographic features and assess the diagnostic value of sonography in the evaluation of children with Henoch-Schonlein purpura. Between October 1993, and Febuary 1998, 67 children with Henoch-Schonlein purpura underwent abdominal ultrasonography, which in 13 was used for follow up. Bowel wall thickness and location, pattern of color Doppler signal in the thickened bowel wall, the size and location of enlarged mesenteric lymph node and the presence of ascites were evaluated. In 42 cases(63%), sonographic findings were positive, and indicated mesenteric lymphadenopathy(n=3D21), small bowel wall thickening(n=3D20), and ascites(n=3D17). Thickened bowels were demonstrated at the ileum in 11 cases, the jejunum in five, the duodenum in one, and combined wall thickening at the duodenum and jejunum in two;thickening of the duodenum and ileum was seen in one case. Thickness varied from 3 to 10 mm(mean:6.5 mm). On follow-up sonography, regression of bowel wall thickening was observed earlier than that of mesenteric lymphadenopathy or ascites, and correlated well with improved abdominal symptoms. Abdominal ultrasonographic manifestations of Henoch-Schonlein purpura were bowel wall thickening, mesentric lymphadenopathy and ascites. Sonography was a simple and useful method for the evaluation of gastrointestinal manifestation of Henoch-Schonlein purpura.=20

Defect detection on videos using neural network

Directory of Open Access Journals (Sweden)

Sizyakin Roman

2017-01-01

Full Text Available In this paper, we consider a method for defects detection in a video sequence, which consists of three main steps; frame compensation, preprocessing by a detector, which is base on the ranking of pixel values, and the classification of all pixels having anomalous values using convolutional neural networks. The effectiveness of the proposed method shown in comparison with the known techniques on several frames of the video sequence with damaged in natural conditions. The analysis of the obtained results indicates the high efficiency of the proposed method. The additional use of machine learning as postprocessing significantly reduce the likelihood of false alarm.

Neural Network in Fixed Time for Collision Detection between Two Convex Polyhedra

OpenAIRE

M. Khouil; N. Saber; M. Mestari

2014-01-01

In this paper, a different architecture of a collision detection neural network (DCNN) is developed. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons, linear and threshold logic, which simplified the actual implementation of all the networks proposed. The study of the collision detection is divided into two sections, the coll...

T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos

OpenAIRE

Kang, Kai; Li, Hongsheng; Yan, Junjie; Zeng, Xingyu; Yang, Bin; Xiao, Tong; Zhang, Cong; Wang, Zhe; Wang, Ruohui; Wang, Xiaogang; Ouyang, Wanli

2016-01-01

The state-of-the-art performance for object detection has been significantly improved over the past two years. Besides the introduction of powerful deep neural networks such as GoogleNet and VGG, novel object detection frameworks such as R-CNN and its successors, Fast R-CNN and Faster R-CNN, play an essential role in improving the state-of-the-art. Despite their effectiveness on still images, those frameworks are not specifically designed for object detection from videos. Temporal and context...

The Application of Helicopter Rotor Defect Detection Using Wavelet Analysis and Neural Network Technique

Directory of Open Access Journals (Sweden)

Jin-Li Sun

2014-06-01

Full Text Available When detect the helicopter rotor beam with ultrasonic testing, it is difficult to realize the noise removing and quantitative testing. This paper used the wavelet analysis technique to remove the noise among the ultrasonic detection signal and highlight the signal feature of defect, then drew the curve of defect size and signal amplitude. Based on the relationship of defect size and signal amplitude, a BP neural network was built up and the corresponding estimated value of the simulate defect was obtained by repeating training. It was confirmed that the wavelet analysis and neural network technique met the requirements of practical testing.

Clinical and ultrasonographic features of amoebic liver abscess In a ...

African Journals Online (AJOL)

Background: Amoebic Liver abscess is a tropical disease with a wide spectrum of clinical presentation. This study describes its clinical and ultrasonographic features in a teaching hospital setting. Methods: Records of all patients aged 18 years and above with amoebic liver abscess admitted in the medical wards of ...

Convolutional neural networks for segmentation and object detection of human semen

DEFF Research Database (Denmark)

Nissen, Malte Stær; Krause, Oswin; Almstrup, Kristian

2017-01-01

We compare a set of convolutional neural network (CNN) architectures for the task of segmenting and detecting human sperm cells in an image taken from a semen sample. In contrast to previous work, samples are not stained or washed to allow for full sperm quality analysis, making analysis harder due...

Utility of Vascular Enhancement Technique (ClarifyTM) in Ultrasonographic Evaluation of Abdominal Vessels

International Nuclear Information System (INIS)

Oh, Jong Young; Cho, Jin Han; Choi, Jong Cheol; Shin, Tae Beom; Lee, Jin Hwa; Yoon, Seong Kuk; Nam, Kyung Jin

2006-01-01

Vascular enhancement (VE) technology(ClarifyTM) is a new technique in vascular, B-mode imaging. The purpose of this study was to evaluate the value of VE technology in ultrasonographic diagnosis of abdominal vasculature. Seventy-one adult patients (39 men and 32 women: age range, 25-89 years: mean age, 56 years) who had undergone abdominal ultrasonography were included in this study. The imaging was performed with a 1.8-4.0 MHz convex array transducer (SONOLINE, Antares, Siemens Medical Solutions, WA) by an abdominal radiologist. The radiologist obtained images of the same vascular area with each of conventional ultrasonography imaging (CUS), tissue harmonic imaging (THI), CUS plus VE technique and THI plus VE technique. Images were divided into normal (56) and abnormal (15) groups. The vessel visibility, conspicuity of the vascular wall and contrast resolution with adjacent structures were evaluated in the normal group, and the lesion conspicuity and border sharpness were evaluated in the abnormal group. On the PACS monitor, the images were graded into four grades by two radiologists in consensus. Statistical analysis was performed using Wilcoxon signed rank test. In the normal group, all parameters of the ultrasonographic imaging which applied the VE technique were superior to those of the imaging without VE technique (p < 0.05). In the abnormal group, combined use of VE technique with CUS or THI provided better results than CUS or THI alone in terms of lesion conspicuity and border sharpness (p < 0.05). THI combined with VE technique provided the best image quality among the 4 ultrasonographic methods examined in this study for the evaluation of both normal and abnormal abdominal vessels (p < 0.05). VE technology was a helpful technique to evaluate the abdominal vasculature. Furthermore, VE technique combined with THI provided better image quality than other ultrasonographic methods in the evaluation of abdominal vessels

Improving inter-observer variability in the evaluation of ultrasonographic features of polycystic ovaries

Directory of Open Access Journals (Sweden)

Leswick David A

2008-07-01

Full Text Available Abstract Background We recently reported poor inter-observer agreement in identifying and quantifying individual ultrasonographic features of polycystic ovaries. Our objective was to determine the effect of a training workshop on reducing inter-observer variation in the ultrasonographic evaluation of polycystic ovaries. Methods Transvaginal ultrasound recordings from thirty women with polycystic ovary syndrome (PCOS were evaluated by three radiologists and three reproductive endocrinologists both before and after an ultrasound workshop. The following endpoints were assessed: 1 follicle number per ovary (FNPO, 2 follicle number per single cross-section (FNPS, 3 largest follicle diameter, 4 ovarian volume, 5 follicle distribution pattern and 6 presence of a corpus luteum (CL. Lin's concordance correlation coefficients (rho and kappa statistics for multiple raters (kappa were used to assess level of inter-observer agreement (>0.80 good, 0.60 – 0.80 moderate/fair, Results Following the workshop, inter-observer agreement improved for the evaluation of FNPS (rho = 0.70, delta rho = +0.11, largest follicle diameter (rho = 0.77, delta rho = +0.10, ovarian volume (rho = 0.84, delta rho = +0.12, follicle distribution pattern (kappa = 0.80, delta kappa = +0.21 and presence of a CL (kappa = 0.87, delta kappa = +0.05. No improvement was evident for FNPO (rho = 0.54, delta rho = -0.01. Both radiologists and reproductive endocrinologists demonstrated improvement in scores (p Conclusion Reliability in evaluating ultrasonographic features of polycystic ovaries can be significantly improved following participation in a training workshop. If ultrasonographic evidence of polycystic ovaries is to be used as an objective measure in the diagnosis of PCOS, then standardized training modules should be implemented to unify the approach to evaluating polycystic ovarian morphology.

ConvNetQuake: Convolutional Neural Network for Earthquake Detection and Location

Science.gov (United States)

Denolle, M.; Perol, T.; Gharbi, M.

2017-12-01

Over the last decades, the volume of seismic data has increased exponentially, creating a need for efficient algorithms to reliably detect and locate earthquakes. Today's most elaborate methods scan through the plethora of continuous seismic records, searching for repeating seismic signals. In this work, we leverage the recent advances in artificial intelligence and present ConvNetQuake, a highly scalable convolutional neural network for probabilistic earthquake detection and location from single stations. We apply our technique to study two years of induced seismicity in Oklahoma (USA). We detect 20 times more earthquakes than previously cataloged by the Oklahoma Geological Survey. Our algorithm detection performances are at least one order of magnitude faster than other established methods.

Anomaly based intrusion detection for a biometric identification system using neural networks

CSIR Research Space (South Africa)

Mgabile, T

2012-10-01

Full Text Available detection technique that analyses the fingerprint biometric network traffic for evidence of intrusion. The neural network algorithm that imitates the way a human brain works is used in this study to classify normal traffic and learn the correct traffic...

Detection of directional eye movements based on the electrooculogram signals through an artificial neural network

International Nuclear Information System (INIS)

Erkaymaz, Hande; Ozer, Mahmut; Orak, İlhami Muharrem

2015-01-01

The electrooculogram signals are very important at extracting information about detection of directional eye movements. Therefore, in this study, we propose a new intelligent detection model involving an artificial neural network for the eye movements based on the electrooculogram signals. In addition to conventional eye movements, our model also involves the detection of tic and blinking of an eye. We extract only two features from the electrooculogram signals, and use them as inputs for a feed-forwarded artificial neural network. We develop a new approach to compute these two features, which we call it as a movement range. The results suggest that the proposed model have a potential to become a new tool to determine the directional eye movements accurately

Online Particle Detection by Neural Networks Based on Topologic Calorimetry Information

CERN Document Server

Ciodaro, T; The ATLAS collaboration; Damazio, D; de Seixas, JM

2011-01-01

This paper presents the last results from the Ringer algorithm, which is based on artificial neural networks for the electron identification at the online filtering system of the ATLAS particle detector, in the context of the LHC experiment at CERN. The algorithm performs topological feature extraction over the ATLAS calorimetry information (energy measurements). Later, the extracted information is presented to a neural network classifier. Studies showed that the Ringer algorithm achieves high detection efficiency, while keeping the false alarm rate low. Optimizations, guided by detailed analysis, reduced the algorithm execution time in 59%. Also, the payload necessary to store the Ringer algorithm information represents less than 6.2 percent of the total filtering system amount

Tactile detection of slip: surface microgeometry and peripheral neural codes.

Science.gov (United States)

Srinivasan, M A; Whitehouse, J M; LaMotte, R H

1990-06-01

1. The role of the microgeometry of planar surfaces in the detection of sliding of the surfaces on human and monkey fingerpads was investigated. By the use of a servo-controlled tactile stimulator to press and stroke glass plates on passive fingerpads of human subjects, the ability of humans to discriminate the direction of skin stretch caused by friction and to detect the sliding motion (slip) of the plates with or without micrometer-sized surface features was determined. To identify the associated peripheral neural codes, evoked responses to the same stimuli were recorded from single, low-threshold mechanoreceptive afferent fibers innervating the fingerpads of anesthetized macaque monkeys. 2. Humans could not detect the slip of a smooth glass plate on the fingerpad. However, the direction of skin stretch was perceived based on the information conveyed by the slowly adapting afferents that respond differentially to the stretch directions. Whereas the direction of skin stretch signaled the direction of impending slip, the perception of relative motion between the plate and the finger required the existence of detectable surface features. 3. Barely detectable micrometer-sized protrusions on smooth surfaces led to the detection of slip of these surfaces, because of the exclusive activation of rapidly adapting fibers of either the Meissner (RA) or the Pacinian (PC) type to specific geometries of the microfeatures. The motion of a smooth plate with a very small single raised dot (4 microns high, 550 microns diam) caused the sequential activation of neighboring RAs along the dot path, thus providing a reliable spatiotemporal code. The stroking of the plate with a fine homogeneous texture composed of a matrix of dots (1 microns high, 50 microns diam, and spaced at 100 microns center-to-center) induced vibrations in the fingerpad that activated only the PCs and resulted in an intensive code. 4. The results show that surprisingly small features on smooth surfaces are

Detection of Pistachio Aflatoxin Using Raman Spectroscopy and Artificial Neural Networks

Directory of Open Access Journals (Sweden)

R Mohammadigol

2015-03-01

Full Text Available Pistachio contamination to aflatoxin has been known as a serious problem for pistachio exportation. With regards to the increasing demand for Raman spectroscopy to detect and classify different materials and also the current experimental and technical problems for measuring toxin (such as being expensive and time-consuming, the main objective of this study was to detect aflatoxin contamination in pistachio by using Raman spectroscopy technique and artificial neural networks. Three sets of samples were prepared: non-contaminated (healthy and contaminated samples with 20 and 100 ppb of the total aflatoxins (B1+B2+G1+G2. After spectral acquisition, considering to the results, spectral data were normalized and then principal components (PCs were extracted to reduce the data dimensions. For classification of the samples spectra, an artificial neural network was used with a feed forward back propagation algorithm for 4 inputs and 3 neurons in hidden layer. Mean overall accuracy was achieved to be 98 percent; therefore, non-liner Raman spectra data modeling by ANN for samples classification was successful.

Clinical, ultrasonographic, and laboratory findings in 12 llamas and 12 alpacas with malignant round cell tumors

Science.gov (United States)

Martin, Jeanne M.; Valentine, Beth A.; Cebra, Christopher K.

2010-01-01

Clinical signs, duration of illness, clinicopathologic findings, and ultrasonographic findings were evaluated in 12 llamas and 12 alpacas with malignant round cell tumors (MRCT). All but 1 animal died or was euthanized. Common clinical findings were anorexia, recumbency or weakness, and weight loss or poor growth. Peripheral lymphadenomegaly occurred in only 7 animals and was detected more often at necropsy than during physical examination. Common clinicopathologic abnormalities were hypoalbuminemia, acidosis, azotemia, anemia, hyperglycemia, and neutrophilia. Ultrasonography detected tumors in 4/6 animals. Cytologic evaluation of fluid or tissue aspirates or histopathology of biopsy tissue was diagnostic in 5/6 cases. A clinical course of 2 wk or less prior to death or euthanasia was more common in animals ≤ 2 y of age (9/11) than in older animals (6/13). Regular examination of camelids to include clinical pathology and evaluation of peripheral lymph nodes may result in early detection of MCRT. PMID:21358931

Brain ultrasonographic findings of late-onset circulatory dysfunction due to adrenal insufficiency in preterm infants

Energy Technology Data Exchange (ETDEWEB)

Shin, Su Mi; Chai, Jee Won [Dept. of Radiology, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul (Korea, Republic of)

2016-07-15

The aim of this study was to characterize the brain ultrasonographic findings of late-onset circulatory dysfunction (LCD) due to adrenal insufficiency (AI) in preterm infants. Among the 257 preterm infants born at <33 weeks of gestation between December 2009 and February 2014 at our institution, 35 preterm infants were diagnosed with AI. Brain ultrasonographic findings were retrospectively analyzed before and after LCD in 14 preterm infants, after exclusion of the other 21 infants with AI due to the following causes: death (n=2), early AI (n=5), sepsis (n=1), and patent ductus arteriosus (n=13). Fourteen of 257 infants (5.4%) were diagnosed with LCD due to AI. The age at LCD was a median of 18.5 days (range, 9 to 32 days). The last ultrasonographic findings before LCD occurred showed grade 1 periventricular echogenicity (PVE) in all 14 patients and germinal matrix hemorrhage (GMH) with focal cystic change in one patient. Ultrasonographic findings after LCD demonstrated no significant change in grade 1 PVE and no new lesions in eight (57%), grade 1 PVE with newly appearing GMH in three (21%), and increased PVE in three (21%) infants. Five infants (36%) showed new development (n=4) or increased size (n=1) of GMH. Two of three infants (14%) with increased PVE developed cystic periventricular leukomalacia (PVL) and rapid progression to macrocystic encephalomalacia. LCD due to AI may be associated with the late development of GMH, increased PVE after LCD, and cystic PVL with rapid progression to macrocystic encephalomalacia.

Using Hybrid Algorithm to Improve Intrusion Detection in Multi Layer Feed Forward Neural Networks

Science.gov (United States)

Ray, Loye Lynn

2014-01-01

The need for detecting malicious behavior on a computer networks continued to be important to maintaining a safe and secure environment. The purpose of this study was to determine the relationship of multilayer feed forward neural network architecture to the ability of detecting abnormal behavior in networks. This involved building, training, and…

Detection and diagnosis of colitis on computed tomography using deep convolutional neural networks.

Science.gov (United States)

Liu, Jiamin; Wang, David; Lu, Le; Wei, Zhuoshi; Kim, Lauren; Turkbey, Evrim B; Sahiner, Berkman; Petrick, Nicholas A; Summers, Ronald M

2017-09-01

Colitis refers to inflammation of the inner lining of the colon that is frequently associated with infection and allergic reactions. In this paper, we propose deep convolutional neural networks methods for lesion-level colitis detection and a support vector machine (SVM) classifier for patient-level colitis diagnosis on routine abdominal CT scans. The recently developed Faster Region-based Convolutional Neural Network (Faster RCNN) is utilized for lesion-level colitis detection. For each 2D slice, rectangular region proposals are generated by region proposal networks (RPN). Then, each region proposal is jointly classified and refined by a softmax classifier and bounding-box regressor. Two convolutional neural networks, eight layers of ZF net and 16 layers of VGG net are compared for colitis detection. Finally, for each patient, the detections on all 2D slices are collected and a SVM classifier is applied to develop a patient-level diagnosis. We trained and evaluated our method with 80 colitis patients and 80 normal cases using 4 × 4-fold cross validation. For lesion-level colitis detection, with ZF net, the mean of average precisions (mAP) were 48.7% and 50.9% for RCNN and Faster RCNN, respectively. The detection system achieved sensitivities of 51.4% and 54.0% at two false positives per patient for RCNN and Faster RCNN, respectively. With VGG net, Faster RCNN increased the mAP to 56.9% and increased the sensitivity to 58.4% at two false positive per patient. For patient-level colitis diagnosis, with ZF net, the average areas under the ROC curve (AUC) were 0.978 ± 0.009 and 0.984 ± 0.008 for RCNN and Faster RCNN method, respectively. The difference was not statistically significant with P = 0.18. At the optimal operating point, the RCNN method correctly identified 90.4% (72.3/80) of the colitis patients and 94.0% (75.2/80) of normal cases. The sensitivity improved to 91.6% (73.3/80) and the specificity improved to 95.0% (76.0/80) for the Faster RCNN

Salient regions detection using convolutional neural networks and color volume

Science.gov (United States)

Liu, Guang-Hai; Hou, Yingkun

2018-03-01

Convolutional neural network is an important technique in machine learning, pattern recognition and image processing. In order to reduce the computational burden and extend the classical LeNet-5 model to the field of saliency detection, we propose a simple and novel computing model based on LeNet-5 network. In the proposed model, hue, saturation and intensity are utilized to extract depth cues, and then we integrate depth cues and color volume to saliency detection following the basic structure of the feature integration theory. Experimental results show that the proposed computing model outperforms some existing state-of-the-art methods on MSRA1000 and ECSSD datasets.

Evaluation of the prenatal diagnosis of neural tube defects by fetal ultrasonographic examination in different centres across Europe

NARCIS (Netherlands)

Boyd, PA; Wellesley, DG; De Walle, HEK; Tenconi, R; Garcia-Minaur, S; Zandwijken, GRJ; Stoll, C; Clementi, M

2000-01-01

Objective-Evaluation of prenatal diagnosis of neural tube defects by ultrasound examination in unselected populations across Europe. Setting-Prenatal ultrasound units in areas that report to contributing congenital malformation registers. Methods-All cases with a suspected or confirmed neural tube

The vanishing twin: morphologic and cytogenetic evaluation of an ultrasonographic phenomenon

DEFF Research Database (Denmark)

Rudnicki, M; Vejerslev, L O; Junge, Jette

1991-01-01

Twin pregnancy was observed by ultrasonographic examination in the 6th week of gestation. After singleton term delivery a thickening of the membranes opposite to the main placenta showed degenerated chorionic villi embedded between one layer of amnion and chorion; no fetal parts were observed. Vi...

Microaneurysm detection using fully convolutional neural networks.

Science.gov (United States)

Chudzik, Piotr; Majumdar, Somshubra; Calivá, Francesco; Al-Diri, Bashir; Hunter, Andrew

2018-05-01

Diabetic retinopathy is a microvascular complication of diabetes that can lead to sight loss if treated not early enough. Microaneurysms are the earliest clinical signs of diabetic retinopathy. This paper presents an automatic method for detecting microaneurysms in fundus photographies. A novel patch-based fully convolutional neural network with batch normalization layers and Dice loss function is proposed. Compared to other methods that require up to five processing stages, it requires only three. Furthermore, to the best of the authors' knowledge, this is the first paper that shows how to successfully transfer knowledge between datasets in the microaneurysm detection domain. The proposed method was evaluated using three publicly available and widely used datasets: E-Ophtha, DIARETDB1, and ROC. It achieved better results than state-of-the-art methods using the FROC metric. The proposed algorithm accomplished highest sensitivities for low false positive rates, which is particularly important for screening purposes. Performance, simplicity, and robustness of the proposed method demonstrates its suitability for diabetic retinopathy screening applications. Copyright © 2018 Elsevier B.V. All rights reserved.

Ultrasonographic findings of mesenchymal chondrosarcoma of the mandible: report of a case

Energy Technology Data Exchange (ETDEWEB)

Shakibafard, Alireza [TABA Medical Imaging Center, Shiraz (Iran, Islamic Republic of); Shahidi, Shoaleh; Zamiri, Barbod; Houshyar, Maneli; Amanpour, Sara [Dental School, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of); Houshyar, Maral [Dental Center of Dastgheib Hospital, Shiraz(Iran, Islamic Republic of)

2012-06-15

Today, ultrasound imaging is being widely used to assess soft tissue lesions in the maxillofacial region. However, ultrasound investigations of intra-osseous lesions are rare, especially for tumors of the jaws. This report emphasized the capability of this useful imaging modality in identification of the characteristics of malignant conditions involving the bone. Mesenchymal chondrosarcoama, one of the unusual malignant conditions of the jaw, was presented in a young male with significant facial swelling. Different imaging modalities parallel with the histopathologic investigation confirmed the diagnosis. Interestingly, destruction of the bony cortex and new bone formation with a characteristic 'sun ray appearance', highly suggestive of sarcomas, was manifested on the ultrasonograph. Thus, this report presented the ultrasonographic features of chondrosarcoma of mandible and considered the ultrasonography to be a useful imaging modality to evaluate intra-osseous jaw lesions.

Clinical, haematobiochemical, radiographic and ultrasonographic features of traumatic reticuloperitonitis in bovines

International Nuclear Information System (INIS)

Mohindroo, J.; Singh, Kiranjeet; Kumar, Ashwani; Randhawa, C.S.

2010-01-01

Study was conducted to compare the clinical, haematobiochemical, radiographic and ultrasonographic features of traumatic reticuloperitonitis in bovines. Clinical cases (4 cows and 17 buffaloes) presented with a history of anorexia, fever, decreased milk yield and loss of defecation/scant faeces, were used. Haematological picture revealed neutrophilic leucocytosis with left shift and blood biochemical status showed elevated levels of total protein, albumin, and fibrinogen. Decreased plasma concentration of sodium, potassium and chloride was observed in majority of the cases. Radiographic examination revealed presence of multiple metallic foreign densities in the reticulum of the bovines. Ultrasonographically, morphological changes of reticular wall and reticulophrenic adhesions in cases of localised peritonitis were visualized. The presence of anechoic fluid without echogenic margins, not restricted to reticulum and sometimes with floating fibrinous shreds was observed in cases of diffuse peritonitis. Ultrasonography in B mode and B+ mode found helpful for the diagnosis of traumatic reticuloperitonitis and differentiation of localised peritonitis from diffuse peritonitis

Ultrasonographic evaluation of the iatrogenic peripheral nerve injuries in upper extremity

International Nuclear Information System (INIS)

Karabay, Nuri; Toros, Tulgar; Ademoglu, Yalcin; Ada, Sait

2010-01-01

The aim of our study is to assess the efficiency of the ultrasonography (US) in the diagnosis of peripheral nerve injury. This study includes nine patients (six radial, one median and two posterior interosseous (PIO) nerves) with peripheral nerve injury diagnosed by clinical and electrophysiological methods in the last 3 years. Preoperatively, an ultrasonographic examination was performed and correlated with physical exam and surgical findings. Five patients, who were diagnosed as peripheral nerve transection by US, underwent surgery. The ultrasonographic findings were concordant with the intraoperative findings. Axonal swelling alone was found in the remaining three patients, who were treated conservatively because of preserved nerve continuity without display of nerve compression. In one patient, we were unable to visualize the nerve due to obesity and soft tissue edema. High-resolution US provide morphological information about the exact location, intensity and extent of the nerve injuries, facilitating the preoperative diagnosis. Thus, US may be a useful method for planning optimal treatment strategy in especially iatrogenic nerve injuries.

Ultrasonographic evaluation of the iatrogenic peripheral nerve injuries in upper extremity

Energy Technology Data Exchange (ETDEWEB)

Karabay, Nuri [Department of Radiology, Hand and Microsurgery and Orthopaedics and Traumatology (EMOT) Hospital, 1418 Sok. No: 14 Kahramanlar, 35230 Izmir (Turkey)], E-mail: nurikarabay@gmail.com; Toros, Tulgar [Department of Orthopaedics and Traumatology, Hand and Microsurgery and Orthopaedics and Traumatology (EMOT) Hospital, 1418 Sok. No: 14 Kahramanlar, 35230 Izmir (Turkey)], E-mail: tulgartoros@yahoo.com; Ademoglu, Yalcin [Department of Orthopaedics and Traumatology, Hand and Microsurgery and Orthopaedics and Traumatology (EMOT) Hospital, 1418 Sok. No: 14 Kahramanlar, 35230 Izmir (Turkey)], E-mail: yalcinademoglu@yahoo.com; Ada, Sait [Department of Orthopaedics and Traumatology, Hand and Microsurgery and Orthopaedics and Traumatology (EMOT) Hospital, 1418 Sok. No: 14 Kahramanlar, 35230 Izmir (Turkey)], E-mail: sait_ada@yahoo.com

2010-02-15

The aim of our study is to assess the efficiency of the ultrasonography (US) in the diagnosis of peripheral nerve injury. This study includes nine patients (six radial, one median and two posterior interosseous (PIO) nerves) with peripheral nerve injury diagnosed by clinical and electrophysiological methods in the last 3 years. Preoperatively, an ultrasonographic examination was performed and correlated with physical exam and surgical findings. Five patients, who were diagnosed as peripheral nerve transection by US, underwent surgery. The ultrasonographic findings were concordant with the intraoperative findings. Axonal swelling alone was found in the remaining three patients, who were treated conservatively because of preserved nerve continuity without display of nerve compression. In one patient, we were unable to visualize the nerve due to obesity and soft tissue edema. High-resolution US provide morphological information about the exact location, intensity and extent of the nerve injuries, facilitating the preoperative diagnosis. Thus, US may be a useful method for planning optimal treatment strategy in especially iatrogenic nerve injuries.

CONEDEP: COnvolutional Neural network based Earthquake DEtection and Phase Picking

Science.gov (United States)

Zhou, Y.; Huang, Y.; Yue, H.; Zhou, S.; An, S.; Yun, N.

2017-12-01

We developed an automatic local earthquake detection and phase picking algorithm based on Fully Convolutional Neural network (FCN). The FCN algorithm detects and segments certain features (phases) in 3 component seismograms to realize efficient picking. We use STA/LTA algorithm and template matching algorithm to construct the training set from seismograms recorded 1 month before and after the Wenchuan earthquake. Precise P and S phases are identified and labeled to construct the training set. Noise data are produced by combining back-ground noise and artificial synthetic noise to form the equivalent scale of noise set as the signal set. Training is performed on GPUs to achieve efficient convergence. Our algorithm has significantly improved performance in terms of the detection rate and precision in comparison with STA/LTA and template matching algorithms.

Efficient airport detection using region-based fully convolutional neural networks

Science.gov (United States)

Xin, Peng; Xu, Yuelei; Zhang, Xulei; Ma, Shiping; Li, Shuai; Lv, Chao

2018-04-01

This paper presents a model for airport detection using region-based fully convolutional neural networks. To achieve fast detection with high accuracy, we shared the conv layers between the region proposal procedure and the airport detection procedure and used graphics processing units (GPUs) to speed up the training and testing time. For lack of labeled data, we transferred the convolutional layers of ZF net pretrained by ImageNet to initialize the shared convolutional layers, then we retrained the model using the alternating optimization training strategy. The proposed model has been tested on an airport dataset consisting of 600 images. Experiments show that the proposed method can distinguish airports in our dataset from similar background scenes almost real-time with high accuracy, which is much better than traditional methods.

Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

Science.gov (United States)

Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude

2010-02-01

Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

Is Neural Activity Detected by ERP-Based Brain-Computer Interfaces Task Specific?

Directory of Open Access Journals (Sweden)

Markus A Wenzel

Full Text Available Brain-computer interfaces (BCIs that are based on event-related potentials (ERPs can estimate to which stimulus a user pays particular attention. In typical BCIs, the user silently counts the selected stimulus (which is repeatedly presented among other stimuli in order to focus the attention. The stimulus of interest is then inferred from the electroencephalogram (EEG. Detecting attention allocation implicitly could be also beneficial for human-computer interaction (HCI, because it would allow software to adapt to the user's interest. However, a counting task would be inappropriate for the envisaged implicit application in HCI. Therefore, the question was addressed if the detectable neural activity is specific for silent counting, or if it can be evoked also by other tasks that direct the attention to certain stimuli.Thirteen people performed a silent counting, an arithmetic and a memory task. The tasks required the subjects to pay particular attention to target stimuli of a random color. The stimulus presentation was the same in all three tasks, which allowed a direct comparison of the experimental conditions.Classifiers that were trained to detect the targets in one task, according to patterns present in the EEG signal, could detect targets in all other tasks (irrespective of some task-related differences in the EEG.The neural activity detected by the classifiers is not strictly task specific but can be generalized over tasks and is presumably a result of the attention allocation or of the augmented workload. The results may hold promise for the transfer of classification algorithms from BCI research to implicit relevance detection in HCI.

Ultrasonographic Findings of Hepatic Hemangioma : Analysis of Echo-Patterns According to Tumor Size

International Nuclear Information System (INIS)

Lee, Kang Hoon; Lee, Hae Giu; Choi, Byung Gil; Jung, Jung Im; Lee, Sung Yong; Yim, Jung Ik; Shinn, Kyung Sub

1995-01-01

We performed a retrospective analysis of ultrasonographic features of hepatic hemangiomas according to tumor size. After an initial ultrasonographic examination, 5l hepatic hemangiomas in 4l patients were confirmed by one or combined examinations of 99mTc RBC SPECT, computed tomography, magnetic resonance imaging. angiography or ultrasonographic follow up. Definition of margin, internal echogenicity,peripheral rim and posterior enhancement were evaluated by two radiologists. Forty seven cases(92%) of 51 hemangiomas appeared to be well defined. Of 29 hemangiomas with less than 3cm in diameter. 25 cases (86%)showed homogeneous internal echogenicity. Of 22 hemangiomas with above 3 cm in diameter, 16 cases (73%) showed inhomogeneous echogenicity. Of 12 hemangiomas (24%) with peripheral rim, nine cases revealed hyperechoic rim and two hypoechoic rim. The remaining one case showed hyperechoic rim and hypoechoic rim alternately. Hemangiomas with greater than 3cm in diameter had higher incidence of inhomogeneous echogenicity, peripheral rim and posterior enhancement than those less than 3 cm(P<0.05). The majority of small hepatic hemangiomas are well defined homogeneous hyperechoic masses. On the other hand, large hemangiomas tended to have higher incidence of inhomogeneous internal echogenicity, posterior enhancement and a peripheral hyperechoic rim. A hyperechoic mass with a hypoechoic rim should also be considered as a candidate for hepatic hemangioma

[Ultrasonographic Findings of Cervical Lymphadenopathy with Infectious Mononucleosis].

Science.gov (United States)

Fu, Xian-Shui; Ren, Liu-Qiong; Yang, Li-Juan; Lü, Ke; Chen, Yuan-Yuan; Li, Zhen-Cai

2015-12-01

To evaluate the high-resolution and color Doppler ultrasonographic (US) characteristics of cervical lymphadenopathy in patients with infectious mononucleosis. High-resolution and color Doppler US were performed in 30 patients aged 2 to 30 years with a total of 59 palpable enlarged cervical lymph nodes due to infectious mononucleosis. The US characteristics of the nodes including shape,echotexture,hilum,border,matting,cystic necrosis,calcification and vascular pattern were assessed. Three patients received cervical lymph nodes biopsies. The common US findings of cervical lymphadenopathy due to infectious mononucleosis were round shape (69.5%),bilateral distribution (96.7%),matting (83.3%) [even bilateral matting (66.6%)],indistinct margin (79.7%),absence of hilum (66.1%),heterogeneous echotecture (61.0%),and central hilar vascular pattern(89.8%). In 2 patients with absence of the echoic hilum,lymph nodes biopsies showed histological features including marked effacement of the normal architecture in the medullary region accompanied by a mixed proliferation of lymphocytes and histiocytes. In all infectious mononucleosis nodes with a hilum,85.0% had heterogeneously hypo/iso-echoic hila and indistinct demarcation to the cortex. One of them underwent lymph node biopsy and histological findings showed obvious dilation of the sinus oidal lumen and proliferation of histiocytes. Although several ultrasonographic characteristics frequently present in the nodes of infectious mononucleosis are not specific,the combination of ultrasound findings may be valuable in differential diagnosis.

Ultrasonographic findings in 14 dogs with ectopic ureter

International Nuclear Information System (INIS)

Lamb, C.R.; Gregory, S.P.

1998-01-01

To evaluate ultrasonography as an alternative to contrast radiographyfor diagnosis of ectopic ureter in dogs, ultrasonography of the urinary tract was performed prospectively in a series of urinary incontinent dogs anesthetized for contrast radiography, Fourteen dogs had ectopic ureter based ore surgical, necropsy or unequivocal contrast radiographic findings, There were eight females and six males of a variety of breeds; five were Labrador retrievers, Mean (range) age at the time ofdiagnosis was 1.2 (0.2-4) years for females and 3.5 (0.3-5) for males(p < 0.05). Ectopic ureters were unilateral in five dogs (2 left; 3 right) find bilateral in nine dogs. Both ultrasound images and contrastradiographs were positive for 21 (91%) ectopic ureters; the same two ectopic ureters were not defected using either modality, The termination of each of the five normal ureters was visible on ultrasound images; two (40%) were visible on radiographs, Other ultrasonographic findings included dilatation of the ectopic ureter and/or ipsilateral renal pelvis ill ten (43%) instances, evidence of pyelonephritis in two dogs(with enlargement of the contralateral kidney in one dog), and urethral diverticuli in one dog, Ultrasonography is a practical diagnostic Best for ectopic ureter in clogs. In this series these was close correlation between the ultrasonographic and contrast radiographic findings for each ectopic meter, but ultrasonography enabled more accurate determination of normal ureteral anatomy

Detection and classification of power quality disturbances using S-transform and modular neural network

Energy Technology Data Exchange (ETDEWEB)

Bhende, C.N.; Mishra, S.; Panigrahi, B.K. [Department of Electrical Engineering, Indian Institute of Technology, New Delhi 110016 (India)

2008-01-15

This paper presents an S-transform based modular neural network (NN) classifier for recognition of power quality disturbances. The excellent time - frequency resolution characteristics of the S-transform makes it an attractive candidate for the analysis of power quality (PQ) disturbances under noisy condition and has the ability to detect the disturbance correctly. On the other hand, the performance of wavelet transform (WT) degrades while detecting and localizing the disturbances in the presence of noise. Features extracted by using the S-transform are applied to a modular NN for automatic classification of the PQ disturbances that solves a relatively complex problem by decomposing it into simpler subtasks. Modularity of neural network provides better classification, model complexity reduction and better learning capability, etc. Eleven types of PQ disturbances are considered for the classification. The simulation results show that the combination of the S-transform and a modular NN can effectively detect and classify different power quality disturbances. (author)

The influence of exercise during growth on ultrasonographic parameters of the superficial digital flexor tendon of young Thoroughbred horses.

Science.gov (United States)

Moffat, P A; Firth, E C; Rogers, C W; Smith, R K W; Barneveld, A; Goodship, A E; Kawcak, C E; McIlwraith, C W; van Weeren, P R

2008-03-01

Conditioning by early training may influence the composition of certain musculoskeletal tissues, but very few data exist on its effect during growth on tendon structure and function. To investigate whether conditioning exercise in young foals would lead to any ultrasonographically detectable damage to the superficial digital flexor tendon or an increase in cross-sectional area (CSA). Thirty-three Thoroughbred foals reared at pasture were allocated to 2 groups: control (PASTEX) allowed exercise freely at pasture; and CONDEX, also at pasture, began conditioning exercise from mean age 21 days over 1030 m on a purpose-built oval grass track, for 5 days/week until mean age 18 months. Foals were observed daily, and underwent orthopaedic examination monthly. Ultrasonographic images of the superficial digital flexor tendon (SDFT) at the mid-metacarpal level of both forelimbs were obtained in all foals at ages 5, 8, 12, 15 and 18 months. CSA was validated (r(2) = 0.89) by determining CSA from digital photographs of the transected SDFT surface from 12 of the horses necropsied at age 17.1 months. here was no clinical or ultrasonographic evidence of tendonopathy in either group and the greatest increase in mean CSA in both groups occurred between age 5 and 8 months. Across all age categories, there was no significant difference in mean CSA between the left and right limbs, or colts and fillies; there was a trend towards a larger CSA in the CONDEX group (P = 0.058). There was no conclusive evidence for a structural adaptive hypertrophy of the SDFT, probably because the regimen was insufficiently rigorous or because spontaneous pasture exercise may induce maximal development of energy storing tendons. A moderate amount of early conditioning exercise against a background of constant exercise at pasture is not harmful to the development of the flexor tendons.

Low-complexity object detection with deep convolutional neural network for embedded systems

Science.gov (United States)

Tripathi, Subarna; Kang, Byeongkeun; Dane, Gokce; Nguyen, Truong

2017-09-01

We investigate low-complexity convolutional neural networks (CNNs) for object detection for embedded vision applications. It is well-known that consolidation of an embedded system for CNN-based object detection is more challenging due to computation and memory requirement comparing with problems like image classification. To achieve these requirements, we design and develop an end-to-end TensorFlow (TF)-based fully-convolutional deep neural network for generic object detection task inspired by one of the fastest framework, YOLO.1 The proposed network predicts the localization of every object by regressing the coordinates of the corresponding bounding box as in YOLO. Hence, the network is able to detect any objects without any limitations in the size of the objects. However, unlike YOLO, all the layers in the proposed network is fully-convolutional. Thus, it is able to take input images of any size. We pick face detection as an use case. We evaluate the proposed model for face detection on FDDB dataset and Widerface dataset. As another use case of generic object detection, we evaluate its performance on PASCAL VOC dataset. The experimental results demonstrate that the proposed network can predict object instances of different sizes and poses in a single frame. Moreover, the results show that the proposed method achieves comparative accuracy comparing with the state-of-the-art CNN-based object detection methods while reducing the model size by 3× and memory-BW by 3 - 4× comparing with one of the best real-time CNN-based object detectors, YOLO. Our 8-bit fixed-point TF-model provides additional 4× memory reduction while keeping the accuracy nearly as good as the floating-point model. Moreover, the fixed- point model is capable of achieving 20× faster inference speed comparing with the floating-point model. Thus, the proposed method is promising for embedded implementations.

ID card number detection algorithm based on convolutional neural network

Science.gov (United States)

Zhu, Jian; Ma, Hanjie; Feng, Jie; Dai, Leiyan

2018-04-01

In this paper, a new detection algorithm based on Convolutional Neural Network is presented in order to realize the fast and convenient ID information extraction in multiple scenarios. The algorithm uses the mobile device equipped with Android operating system to locate and extract the ID number; Use the special color distribution of the ID card, select the appropriate channel component; Use the image threshold segmentation, noise processing and morphological processing to take the binary processing for image; At the same time, the image rotation and projection method are used for horizontal correction when image was tilting; Finally, the single character is extracted by the projection method, and recognized by using Convolutional Neural Network. Through test shows that, A single ID number image from the extraction to the identification time is about 80ms, the accuracy rate is about 99%, It can be applied to the actual production and living environment.

Realtime ultrasonographic findings in gallbladder carcinoma

Energy Technology Data Exchange (ETDEWEB)

Ko, Y. T.; Woo, S. K.; Suh, I. J.; Lim, J. H.; Kim, H. K.; Kim, S. Y.; Ahn, C. Y. [Kyung Hee University Hospital, Seoul (Korea, Republic of)

2010-05-15

It is well known that realtime ultrasonography is the primary diagnostic modality to evaluate gallbladder diseases. The authors studied ultrasonographic findings of 10 pathologically proven gallbladder carcinoma patients, and it was compared with the findings of 4 cases of ERCP and 2 cases of CT which were performed at the same period. The results were as follows: 1. They were 6 males and 4 females with over 50 years of age except a 41 year old female. 2. The ultrasonographic classifications of the cases were 4 of fungating mass types, 3 of mass filling gallbladder types, 2 wall thickening types and 1 of mixed type, wall thickening and fungating mass. 3. Seven cases of cholecystitis, 6 cases of intrahepatic biliary duct dilatation, 5 cases of gallstone, 4 cases of common bile duct dilatation, 4 cases of sludge bile, 2 cases of gallbladder dilatation, 1 case of right sub phrenic and pericholecystic abscess due to perforated gallbladder. 4. Five cases of mesenteric infiltrations, 3 cases of hepatic infiltration adjacent to gallbladder, 2 cases of lymphatic metastasis to right lobe of liver and 2 cases of pericholedochal and pericaval lymph node metastasis. 5. The indistinct margin between gallbladder and surrounding organ adjacent to gallbladder mass or gallbladder wall thickening suggest cancer infiltration to adjacent organ such as liver or omentum. 6. If gallstone is engulfed in thickened gallbladder wall, the wall thickening suggests gallbladder carcinoma. 7. The differentiation between fungating mass and sludge bile, and the determination of mass could be done by positional change. 8. The preoperative ultrasonic diagnositc accuracy was in 9 out of 10 cases (90%). 9. Because of the frequent cystic duct obstruction by associated inflammation, the diagnostic accuracy of ERCP for gallbladder carcinoma was low.

Realtime ultrasonographic findings in gallbladder carcinoma

International Nuclear Information System (INIS)

Ko, Y. T.; Woo, S. K.; Suh, I. J.; Lim, J. H.; Kim, H. K.; Kim, S. Y.; Ahn, C. Y.

2010-01-01

It is well known that realtime ultrasonography is the primary diagnostic modality to evaluate gallbladder diseases. The authors studied ultrasonographic findings of 10 pathologically proven gallbladder carcinoma patients, and it was compared with the findings of 4 cases of ERCP and 2 cases of CT which were performed at the same period. The results were as follows: 1. They were 6 males and 4 females with over 50 years of age except a 41 year old female. 2. The ultrasonographic classifications of the cases were 4 of fungating mass types, 3 of mass filling gallbladder types, 2 wall thickening types and 1 of mixed type, wall thickening and fungating mass. 3. Seven cases of cholecystitis, 6 cases of intrahepatic biliary duct dilatation, 5 cases of gallstone, 4 cases of common bile duct dilatation, 4 cases of sludge bile, 2 cases of gallbladder dilatation, 1 case of right sub phrenic and pericholecystic abscess due to perforated gallbladder. 4. Five cases of mesenteric infiltrations, 3 cases of hepatic infiltration adjacent to gallbladder, 2 cases of lymphatic metastasis to right lobe of liver and 2 cases of pericholedochal and pericaval lymph node metastasis. 5. The indistinct margin between gallbladder and surrounding organ adjacent to gallbladder mass or gallbladder wall thickening suggest cancer infiltration to adjacent organ such as liver or omentum. 6. If gallstone is engulfed in thickened gallbladder wall, the wall thickening suggests gallbladder carcinoma. 7. The differentiation between fungating mass and sludge bile, and the determination of mass could be done by positional change. 8. The preoperative ultrasonic diagnositc accuracy was in 9 out of 10 cases (90%). 9. Because of the frequent cystic duct obstruction by associated inflammation, the diagnostic accuracy of ERCP for gallbladder carcinoma was low.

Ultrasonographic findings of gynecomastia

Energy Technology Data Exchange (ETDEWEB)

Kim, Ji Hyung; Oh, Ki Keun; Yoon, Choon Sik; Park, Chang Yun [Yongdong Severance Hospital, Seoul (Korea, Republic of)

1993-12-15

The purposes of our study were to find out characteristic ultrasonographic findings of gynecomastia and to analyze age distribution, causative factors of gynecomastia. For these purposes, medical records of 39 male patients with gynecomastia were reviewed and sonographic findings of 13 cases of gentamycin were analyzed. Gynecomastia was found most commonly in teenagers and commonly in twenties. Almostly, it occurred without any evident etiology and classified as idiopathic or pirbuterol type. Less frequently, it occurred due to drug administration, systemic disease, or male hormone deficiency. Unilateral involvement was seen in 29 cases; 17cases involving the left and 12 cases the right. Bilateral involvement was seen in 10 cases. Sonographically,gynecomastia appeared as hypoechoic or intermediate echoic mass with various shape in the subareolar area. One case showed diffuse fatty breast pattern without definable mass. On sonographic evaluation, prominent nipple should not be misinterpreted as a breast mass. For the correct diagnosis of gynecomastia, both side breasts should be evaluated for comparison

Real-time camera-based face detection using a modified LAMSTAR neural network system

Science.gov (United States)

Girado, Javier I.; Sandin, Daniel J.; DeFanti, Thomas A.; Wolf, Laura K.

2003-03-01

This paper describes a cost-effective, real-time (640x480 at 30Hz) upright frontal face detector as part of an ongoing project to develop a video-based, tetherless 3D head position and orientation tracking system. The work is specifically targeted for auto-stereoscopic displays and projection-based virtual reality systems. The proposed face detector is based on a modified LAMSTAR neural network system. At the input stage, after achieving image normalization and equalization, a sub-window analyzes facial features using a neural network. The sub-window is segmented, and each part is fed to a neural network layer consisting of a Kohonen Self-Organizing Map (SOM). The output of the SOM neural networks are interconnected and related by correlation-links, and can hence determine the presence of a face with enough redundancy to provide a high detection rate. To avoid tracking multiple faces simultaneously, the system is initially trained to track only the face centered in a box superimposed on the display. The system is also rotationally and size invariant to a certain degree.

A novel wavelet neural network based pathological stage detection technique for an oral precancerous condition

Science.gov (United States)

Paul, R R; Mukherjee, A; Dutta, P K; Banerjee, S; Pal, M; Chatterjee, J; Chaudhuri, K; Mukkerjee, K

2005-01-01

Aim: To describe a novel neural network based oral precancer (oral submucous fibrosis; OSF) stage detection method. Method: The wavelet coefficients of transmission electron microscopy images of collagen fibres from normal oral submucosa and OSF tissues were used to choose the feature vector which, in turn, was used to train the artificial neural network. Results: The trained network was able to classify normal and oral precancer stages (less advanced and advanced) after obtaining the image as an input. Conclusions: The results obtained from this proposed technique were promising and suggest that with further optimisation this method could be used to detect and stage OSF, and could be adapted for other conditions. PMID:16126873

Patellar tendinopathy in junior basketball players: a controlled clinical and ultrasonographic study of 268 patellar tendons in players aged 14-18 years.

Science.gov (United States)

Cook, J L; Khan, K M; Kiss, Z S; Griffiths, L

2000-08-01

Anterior knee pain is a common presenting complaint amongst adolescent athletes. We hypothesised that patellar tendinopathy may occur at a younger age than is generally recognised. Thus, we studied the patellar tendons in 134 elite 14- to 18-year-old female (n=64) and male (n=70) basketball players and 29 control swimmers (17 female, 12 male) clinically and with ultrasonography. We found that of 268 tendons, 19 (7%) had current patellar tendinopathy on clinical grounds (11% in males, 2% in females). Twenty-six percent of the basketball players' patellar tendons contained an ultrasonographic hypoechoic region. Ultrasonographic abnormality was more prevalent in the oldest tertile of players (17-18 years) than the youngest tertile (14-15.9 years). Of tendons categorised clinically as 'Never patellar tendinopathy', 22% had an ultrasonographic hypoechoic region nevertheless. This study indicates that patellar tendinopathy can occur in 14- to 18-year-old basketball players. Ultrasonographic tendon abnormality is 3 times as common as clinical symptoms.

Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image.

Science.gov (United States)

Xu, Kele; Feng, Dawei; Mi, Haibo

2017-11-23

The automatic detection of diabetic retinopathy is of vital importance, as it is the main cause of irreversible vision loss in the working-age population in the developed world. The early detection of diabetic retinopathy occurrence can be very helpful for clinical treatment; although several different feature extraction approaches have been proposed, the classification task for retinal images is still tedious even for those trained clinicians. Recently, deep convolutional neural networks have manifested superior performance in image classification compared to previous handcrafted feature-based image classification methods. Thus, in this paper, we explored the use of deep convolutional neural network methodology for the automatic classification of diabetic retinopathy using color fundus image, and obtained an accuracy of 94.5% on our dataset, outperforming the results obtained by using classical approaches.

A robust neural network-based approach for microseismic event detection

KAUST Repository

Akram, Jubran

2017-08-17

We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset for event detection. The input features used include the average of absolute amplitudes, variance, energy-ratio and polarization rectilinearity. These features are calculated in a moving-window of same length for the entire waveform. The output is set as a user-specified relative probability curve, which provides a robust way of distinguishing between weak and strong events. An optimal network is selected by studying the weight-based saliency and effect of number of neurons on the predicted results. Using synthetic data examples, we demonstrate that this approach is effective in detecting weaker events and reduces the number of false positives.

Ultrasonographic diagnosis of pyometra in bitches

Directory of Open Access Journals (Sweden)

Nereu Carlos Prestes

1995-06-01

Full Text Available A B-mode ultrasonography (SCANNER 450 (5MHz, Pie Medical, Netherlands was used either alone or associated with laboratorial and radiographic examinations in 33 bitches with clinical diagnosis of pyometra. The increased uterus appeared as a well defined tubular structure with diameter ranging from 0.5 up to 4.0 cm. The uterine lumen was less echoic than the wall, with evident echoic shinings. There was an accordance between the increasing in the viscosity of the vaginal secretion and the echoigenicity. The ultrasonographic diagnosis was possible in 31 bitches (94% confirmed by laparotomy and autopsy. The B-mode ultrasonography can be used in the diagnosis of bitches with pyometra.

Fully automatic oil spill detection from COSMO-SkyMed imagery using a neural network approach

Science.gov (United States)

Avezzano, Ruggero G.; Del Frate, Fabio; Latini, Daniele

2012-09-01

The increased amount of available Synthetic Aperture Radar (SAR) images acquired over the ocean represents an extraordinary potential for improving oil spill detection activities. On the other side this involves a growing workload on the operators at analysis centers. In addition, even if the operators go through extensive training to learn manual oil spill detection, they can provide different and subjective responses. Hence, the upgrade and improvements of algorithms for automatic detection that can help in screening the images and prioritizing the alarms are of great benefit. In the framework of an ASI Announcement of Opportunity for the exploitation of COSMO-SkyMed data, a research activity (ASI contract L/020/09/0) aiming at studying the possibility to use neural networks architectures to set up fully automatic processing chains using COSMO-SkyMed imagery has been carried out and results are presented in this paper. The automatic identification of an oil spill is seen as a three step process based on segmentation, feature extraction and classification. We observed that a PCNN (Pulse Coupled Neural Network) was capable of providing a satisfactory performance in the different dark spots extraction, close to what it would be produced by manual editing. For the classification task a Multi-Layer Perceptron (MLP) Neural Network was employed.

Ultrasonographic, endoscopic and histological appearances of the caecum in cats presenting with chronic clinical signs of caecocolic disease.

Science.gov (United States)

Hahn, Harriet; Pey, Pascaline; Baril, Aurélie; Charpentier, Julie; Desquilbet, Loic; Le Poder, Sophie; Château-Joubert, Sophie; Laloy, Eve; Freiche, Valerie

2017-02-01

Objectives This study aimed to describe the ultrasonographic, endoscopic and histological characteristics of the caecum and ileocaecocolic junction in cats suffering from chronic clinical signs compatible with caecocolic disease. Methods Cats presenting with clinical signs suggestive of a caecocolic disease were prospectively recruited. All cats underwent an ultrasonographic examination of the caecum, ileum, colon, ileocolic lymph nodes and local mesenteric fat, in addition to comprehensive abdominal ultrasonography. This was followed by a colonoscopy with a macroscopic assessment of the caecocolic mucosa; caecocolic tissue samples were systematically collected for histologic analysis. Results Eighteen cats were included. Eleven of 18 cats had ultrasonographic abnormalities adjacent to the ileocaecocolic junction (lymphadenopathy, local steatitis) and 13/18 cats had abnormalities directly related to the junction (wall thickening, loss of wall layering). Seventeen of 18 cats had at least one ultrasonographic abnormality. Endoscopically, hyperaemia, oedema, discoloration and/or erosions were found in all cats. Each cat was classified as having mild or moderate-to-severe lesions according to endoscopic results; no classification could be established statistically for ultrasonographic results. The accentuation of the dimpled pattern tended to be inversely related to the severity of endoscopic lesion scoring. Histologically, a large proportion of cats showed typhlitis (13/16), one had lymphoma and two were normal. All cats with typhlitis also had colitis. There was only slight agreement between endoscopic and histological caecal results regarding the severity of lesions. Loss of caecal wall layering on ultrasound was found in 7/18 cats and, surprisingly, did not appear as a reliable predictor of the severity of inflammation or of malignancy; neither did local steatitis nor lymph node size. Conclusions and relevance Ultrasonography and endoscopy should not be used as the

Neurometaplasticity: Glucoallostasis control of plasticity of the neural networks of error commission, detection, and correction modulates neuroplasticity to influence task precision

Science.gov (United States)

Welcome, Menizibeya O.; Dane, Şenol; Mastorakis, Nikos E.; Pereverzev, Vladimir A.

2017-12-01

The term "metaplasticity" is a recent one, which means plasticity of synaptic plasticity. Correspondingly, neurometaplasticity simply means plasticity of neuroplasticity, indicating that a previous plastic event determines the current plasticity of neurons. Emerging studies suggest that neurometaplasticity underlie many neural activities and neurobehavioral disorders. In our previous work, we indicated that glucoallostasis is essential for the control of plasticity of the neural network that control error commission, detection and correction. Here we review recent works, which suggest that task precision depends on the modulatory effects of neuroplasticity on the neural networks of error commission, detection, and correction. Furthermore, we discuss neurometaplasticity and its role in error commission, detection, and correction.

Breast cancer detection via Hu moment invariant and feedforward neural network

Science.gov (United States)

Zhang, Xiaowei; Yang, Jiquan; Nguyen, Elijah

2018-04-01

One of eight women can get breast cancer during all her life. This study used Hu moment invariant and feedforward neural network to diagnose breast cancer. With the help of K-fold cross validation, we can test the out-of-sample accuracy of our method. Finally, we found that our methods can improve the accuracy of detecting breast cancer and reduce the difficulty of judging.

Pedestrian detection in video surveillance using fully convolutional YOLO neural network

Science.gov (United States)

Molchanov, V. V.; Vishnyakov, B. V.; Vizilter, Y. V.; Vishnyakova, O. V.; Knyaz, V. A.

2017-06-01

More than 80% of video surveillance systems are used for monitoring people. Old human detection algorithms, based on background and foreground modelling, could not even deal with a group of people, to say nothing of a crowd. Recent robust and highly effective pedestrian detection algorithms are a new milestone of video surveillance systems. Based on modern approaches in deep learning, these algorithms produce very discriminative features that can be used for getting robust inference in real visual scenes. They deal with such tasks as distinguishing different persons in a group, overcome problem with sufficient enclosures of human bodies by the foreground, detect various poses of people. In our work we use a new approach which enables to combine detection and classification tasks into one challenge using convolution neural networks. As a start point we choose YOLO CNN, whose authors propose a very efficient way of combining mentioned above tasks by learning a single neural network. This approach showed competitive results with state-of-the-art models such as FAST R-CNN, significantly overcoming them in speed, which allows us to apply it in real time video surveillance and other video monitoring systems. Despite all advantages it suffers from some known drawbacks, related to the fully-connected layers that obstruct applying the CNN to images with different resolution. Also it limits the ability to distinguish small close human figures in groups which is crucial for our tasks since we work with rather low quality images which often include dense small groups of people. In this work we gradually change network architecture to overcome mentioned above problems, train it on a complex pedestrian dataset and finally get the CNN detecting small pedestrians in real scenes.

Learning representations for the early detection of sepsis with deep neural networks.

Science.gov (United States)

Kam, Hye Jin; Kim, Ha Young

2017-10-01

Sepsis is one of the leading causes of death in intensive care unit patients. Early detection of sepsis is vital because mortality increases as the sepsis stage worsens. This study aimed to develop detection models for the early stage of sepsis using deep learning methodologies, and to compare the feasibility and performance of the new deep learning methodology with those of the regression method with conventional temporal feature extraction. Study group selection adhered to the InSight model. The results of the deep learning-based models and the InSight model were compared. With deep feedforward networks, the area under the ROC curve (AUC) of the models were 0.887 and 0.915 for the InSight and the new feature sets, respectively. For the model with the combined feature set, the AUC was the same as that of the basic feature set (0.915). For the long short-term memory model, only the basic feature set was applied and the AUC improved to 0.929 compared with the existing 0.887 of the InSight model. The contributions of this paper can be summarized in three ways: (i) improved performance without feature extraction using domain knowledge, (ii) verification of feature extraction capability of deep neural networks through comparison with reference features, and (iii) improved performance with feedforward neural networks using long short-term memory, a neural network architecture that can learn sequential patterns. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sensitivity and specificity of ultrasonographic features of gout in intercritical and chronic phase.

Science.gov (United States)

Das, Shyamashis; Ghosh, Alakendu; Ghosh, Parasar; Lahiri, Debasish; Sinhamahapatra, Pradyot; Basu, Kaushik

2017-07-01

This study aimed to assess the sensitivity and specificity of ultrasonographic features of gout in intercritical and chronic stages and compared ultrasonographic features of gout between patients with persistent high serum uric acid (SUA) and patients with low SUA. Adult patients with gout confirmed by demonstration of monosodium urate crystals were recruited, if they were in intercritical or chronic stage clinically. Ultrasonographic examination of the first metatarsophalangeal joints (MTPJs) and the knee joints of both sides were done by a blinded rheumatologist trained in musculoskeletal ultrasound. Sixty-two patients with gout and 30 control subjects were examined. The double contour sign (DCS) was found in 71 (57.3%) first MTPJs and tophi were found in 54 (43.5%) first MTPJs. DCS was present in 43 (69.4%) gout patients but none in the control group (P gout patients were 69.4% (56.4-80.4%) and 100% (88.3-100%), respectively, while of tophi they were 66.1% (53-77.7%) and 100% (88.3-100%), respectively. The sensitivity of DCS increased to 100% in high the SUA subgroup (SUA ≥ 7 mg/dL). The low SUA (SUA gout subgroup showed significantly higher occurrence of erosions (40%) and tophi (50%) in first MTP joints than the control group. MSUS is useful for diagnosis of gout in intercritical or chronic stages, especially in patients with persistently high SUA level. © 2016 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.

Role of PGL-I antibody detection in the diagnosis of pure neural leprosy

NARCIS (Netherlands)

Jardim, Marcia R.; Antunes, Sergio L. G.; Simons, Brian; Wildenbeest, Joanne G.; Nery, José Augusto C.; Illarramendi, Ximena; Moraes, Milton O.; Martinez, Alejandra N.; Oskam, Linda; Faber, William R.; Sarno, Euzenir N.; Sampaio, Elizabeth P.; Bührer-Sékula, Samira

2005-01-01

Pure neural leprosy (PNL) is difficult to diagnose because skin lesions and acid-fast bacilli (AFB) in slit smears are absent. At present, the gold standard for PNL diagnosis is the histopathological examination of a peripheral nerve biopsy. Even so, detection of bacteria is difficult and

Convolution neural-network-based detection of lung structures

Science.gov (United States)

Hasegawa, Akira; Lo, Shih-Chung B.; Freedman, Matthew T.; Mun, Seong K.

1994-05-01

Chest radiography is one of the most primary and widely used techniques in diagnostic imaging. Nowadays with the advent of digital radiology, the digital medical image processing techniques for digital chest radiographs have attracted considerable attention, and several studies on the computer-aided diagnosis (CADx) as well as on the conventional image processing techniques for chest radiographs have been reported. In the automatic diagnostic process for chest radiographs, it is important to outline the areas of the lungs, the heart, and the diaphragm. This is because the original chest radiograph is composed of important anatomic structures and, without knowing exact positions of the organs, the automatic diagnosis may result in unexpected detections. The automatic extraction of an anatomical structure from digital chest radiographs can be a useful tool for (1) the evaluation of heart size, (2) automatic detection of interstitial lung diseases, (3) automatic detection of lung nodules, and (4) data compression, etc. Based on the clearly defined boundaries of heart area, rib spaces, rib positions, and rib cage extracted, one should be able to use this information to facilitate the tasks of the CADx on chest radiographs. In this paper, we present an automatic scheme for the detection of lung field from chest radiographs by using a shift-invariant convolution neural network. A novel algorithm for smoothing boundaries of lungs is also presented.

Ultrasonographic Findings of Periappendiceal Abscess

Energy Technology Data Exchange (ETDEWEB)

Woo, Seong Ku; Sung, Dong Wook; Ko, Young Tae; Lim, Jae Hoon; Kim, Soon Yong [Kyung Hee University Hospital, Seoul (Korea, Republic of)

1983-09-15

Although the ultrasonography has been regarded as a important procedure in the diagnosis of intra-abdominal abscess, there were relatively few papers concerning the ultrasonographic findings of perpendicular abscess. Nineteen cases of surgically proven perpendicular abscess caused by perforated appendicitis were studied by ultrasonography at the Kyung Hee University Hospital during last 34 months. The results were as follows: 1. Diagnostic accuracy of the real-time ultrasonography was 94.7% (18/19). There were only one false positive and one false negative. 2. The location of abscesses were; perpendicular 68.4% (13/19), pelvic 21.0% (4/19), sub hepatic 5.3% (1/19) and sub phrenic 5.3% (1/19) in order of frequency. 3. Variable echo-patterns of abscesses was encounted. But irregular, thick walled, posteriorly reinforcing, echo-free or mixed echo-patterns were most common.

Acral melanoma detection using a convolutional neural network for dermoscopy images.

Science.gov (United States)

Yu, Chanki; Yang, Sejung; Kim, Wonoh; Jung, Jinwoong; Chung, Kee-Yang; Lee, Sang Wook; Oh, Byungho

2018-01-01

Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the hands and feet and evaluated its usefulness for the early diagnosis of these conditions. A total of 724 dermoscopy images comprising acral melanoma (350 images from 81 patients) and benign nevi (374 images from 194 patients), and confirmed by histopathological examination, were analyzed in this study. To perform the 2-fold cross validation, we split them into two mutually exclusive subsets: half of the total image dataset was selected for training and the rest for testing, and we calculated the accuracy of diagnosis comparing it with the dermatologist's and non-expert's evaluation. The accuracy (percentage of true positive and true negative from all images) of the convolutional neural network was 83.51% and 80.23%, which was higher than the non-expert's evaluation (67.84%, 62.71%) and close to that of the expert (81.08%, 81.64%). Moreover, the convolutional neural network showed area-under-the-curve values like 0.8, 0.84 and Youden's index like 0.6795, 0.6073, which were similar score with the expert. Although further data analysis is necessary to improve their accuracy, convolutional neural networks would be helpful to detect acral melanoma from dermoscopy images of the hands and feet.

DEVELOPMENT OF WEARABLE HUMAN FALL DETECTION SYSTEM USING MULTILAYER PERCEPTRON NEURAL NETWORK

Directory of Open Access Journals (Sweden)

Hamideh Kerdegari

2013-02-01

Full Text Available This paper presents an accurate wearable fall detection system which can identify the occurrence of falls among elderly population. A waist worn tri-axial accelerometer was used to capture the movement signals of human body. A set of laboratory-based falls and activities of daily living (ADL were performed by volunteers with different physical characteristics. The collected acceleration patterns were classified precisely to fall and ADL using multilayer perceptron (MLP neural network. This work was resulted to a high accuracy wearable fall-detection system with the accuracy of 91.6%.

Sclerosing adenosis: mammographic and ultrasonographic findings with clinical and histopathological correlation

International Nuclear Information System (INIS)

Guenhan-Bilgen, Isil; Memis, Aysenur; Uestuen, Esin Emin; Oezdemir, Necmettin; Erhan, Yildiz

2002-01-01

Objective: To evaluate the mammographic and ultrasonographic findings of sclerosing adenosis, a relatively uncommon entity which may sometimes mimic carcinoma. Materials and methods: A retrospective review of the records of 33700 women, who have undergone mammographic examination at our institution between January 1985 and July 2001 revealed 43 histopathologically proven sclerosing adenosis. The history, physical examination, mammographic and ultrasonographic findings were analyzed in all patients. In 30 patients, the nonpalpable lesions were preoperatively localized by the needle-hookwire system under the guidance of mammography (n=22) or ultrasonography (US) (n=8). Radiological features were correlated with histopathological findings. Results: The age of the patients varied between 32 and 55 years (mean, 43.7 years). Only two patients had a family history of breast cancer. In six patients, the presenting complaint was mastalgia. A palpable mass was present in 13 cases. The mammographic findings were; microcalcifications in 24 (55.8%) (clustered in 22, diffuse in two), mass in five (11.6%), asymmetric focal density in three (6.9%), and focal architectural distortion in three (6.9%) patients. Four of the masses were irregularly contoured, while one was well-circumscribed. On US, focal acoustic shadowing without a mass configuration was noted in the three patients who showed asymmetrical focal density on mammography. In eight patients, who showed normal mammograms, a solid mass was detected on US. Two masses had discrete well-circumscribed oval or lobulated contours, while six showed microlobulation and irregularity. In one case, the irregularly contoured mass had marked posterior acoustic shadowing. Two of the three patients, who had focal architectural distortion on mammograms, had an irregularly contoured solid mass, while the third presented as focal acoustic shadowing without a mass configuration. Conclusion: Sclerosing adenosis mostly presents as a nonpalpable

Ultrasonographic features of the liver with cystic echinococcosis in sheep

Science.gov (United States)

Hussein, Hussein Awad; Elrashidy, Mohammed

2014-01-01

Objectives The present study was designed to gain information about the ultrasonographic features of livers with cystic echinococcosis, as well as to evaluate the use of ultrasonography for diagnosis of such disease in sheep. Design This was a retrospective study during the period April 2011 to March 2013. Participants A total of 22 Baladi sheep (aged three to six years) were included in this study. Based on clear hepatic ultrasonographic findings, all animals were classified into two groups: those with hepatic cysts (n=9) and without liver cysts (healthy liver, n=13). Results Biochemically, serum concentrations of γ-glutamyl transferase, aspartate aminotransferase, total bilirubin and globulins were significantly increased (P<0.01), while albumin was lowered (P<0.01) in sheep with cystic livers. Ultrasonographic findings of diseased sheep livers revealed the presence of rounded, anechoic and unilocular hydatid cysts with ellipse circumference ranged from 6–10 cm. The borders of cysts were mostly well defined. The interior of cysts contained echogenic particulate materials, septations, or fine echoes. At the 10th intercostal space, the ventral margin, size, thickness and angle of livers were higher (P<0.01), while the diameter of portal vein was lower (P<0.01) in sheep with liver cysts than control ones. Furthermore, at the 9th intercostal space, the circumference of the gall bladder was decreased in sheep with hepatic cysts (P<0.01). The sensitivity, specificity, and positive and negative predictive values of ultrasonography for diagnosis of hepatic hydatid cysts were 80 per cent and 100 per cent, and 100 per cent and 83 per cent, respectively. Conclusions Cystic echinococcosis is associated with a number of anatomical alterations in the liver tissues that can be easily recognised by ultrasound. Furthermore, ultrasonography alone or in combination with analysis of biochemical parameters reflecting liver function could be helpful for diagnosis of hepatic

Ultrasonographic features of prenatal testicular torsion: Case report

Directory of Open Access Journals (Sweden)

Elif Ağaçayak

2013-01-01

Full Text Available Although prenatal testicular torsion (PNTT is rarely observed,it is an important condition that can cause bilateralvanishing testis. Generally, PNTT cases observed asextravaginal torsion and treatment is emergency surgicalop-eration. In this article, 39 week presented a case diagnosedin the prenatal testicular torsion. PNTT diagnosiswas confirmed by Doppler ultrasonography and emergencysurgery was performed. Extravaginal left testiculartorsion gangrene and necrosis of the testis was observedin the operation. Left orchiectomy was performed andintrauter-ine ultrasonographic diagnosis was found to becorrect.Key words: Testicular torsion, prenatal diagnosis, features,ultrasonography

Ultrasonographic findings of posterior interosseous nerve syndrome

Energy Technology Data Exchange (ETDEWEB)

Kim, You Dong; Ha, Doo Hoe; Lee, Sang Min [Dept. of Radiology, CHA Bundang Medical Center, CHA University, Seongnam (Korea, Republic of)

2017-10-15

The purpose of this study was to evaluate the ultrasonographic findings associated with posterior interosseous nerve (PIN) syndrome. Approval from the Institutional Review Board was obtained. A retrospective review of 908 patients' sonographic images of the upper extremity from January 2001 to October 2010 revealed 10 patients suspicious for a PIN abnormality (7 male and 3 female patients; mean age of 51.8±13.1 years; age range, 32 to 79 years). The ultrasonographic findings of PIN syndrome, including changes in the PIN and adjacent secondary changes, were evaluated. The anteroposterior diameter of the pathologic PIN was measured in eight patients and the anteroposterior diameter of the contralateral asymptomatic PIN was measured in six patients, all at the level immediately proximal to the proximal supinator border. The size of the pathologic nerves and contralateral asymptomatic nerves was compared using the Mann-Whitney U test. Swelling of the PIN proximal to the supinator canal by compression at the arcade of Fröhse was observed in four cases. Swelling of the PIN distal to the supinator canal was observed in one case. Loss of the perineural fat plane in the supinator canal was observed in one case. Four soft tissue masses were noted. Secondary denervation atrophy of the supinator and extensor muscles was observed in two cases. The mean anteroposterior diameter of the pathologic nerves (n=8, 1.79±0.43 mm) was significantly larger than that of the contralateral asymptomatic nerves (n=6, 1.02±0.22 mm) (P=0.003). Ultrasonography provides high-resolution images of the PIN and helps to diagnose PIN syndrome through visualization of its various causes and adjacent secondary changes.

Recurrent pediatric mesenteroaxial gastric volvulus: case report focusing on ultrasonographic and CT findings

Energy Technology Data Exchange (ETDEWEB)

Choi, Hyun Jun; Yun, Jun Hyun; Choi, Ji Hyeo; Im, Ju Hyun; Kim, Se Jong; Park, Byung Ran [Gwangju Christian Hospital, Gwangju (Korea, Republic of)

2004-11-01

Gastric volvulus is a rare condition, and it is classified as the organoaxial or mesentericaxial type according to the axis of rotation. We experienced 1 case of pediatric recurrent mesenteroaxial gastric volvulus and we report here the ultrasonographic and CT findings.

Recurrent pediatric mesenteroaxial gastric volvulus: case report focusing on ultrasonographic and CT findings

International Nuclear Information System (INIS)

Choi, Hyun Jun; Yun, Jun Hyun; Choi, Ji Hyeo; Im, Ju Hyun; Kim, Se Jong; Park, Byung Ran

2004-01-01

Gastric volvulus is a rare condition, and it is classified as the organoaxial or mesentericaxial type according to the axis of rotation. We experienced 1 case of pediatric recurrent mesenteroaxial gastric volvulus and we report here the ultrasonographic and CT findings

Neural fraud detection in credit card operations.

Science.gov (United States)

Dorronsoro, J R; Ginel, F; Sgnchez, C; Cruz, C S

1997-01-01

This paper presents an online system for fraud detection of credit card operations based on a neural classifier. Since it is installed in a transactional hub for operation distribution, and not on a card-issuing institution, it acts solely on the information of the operation to be rated and of its immediate previous history, and not on historic databases of past cardholder activities. Among the main characteristics of credit card traffic are the great imbalance between proper and fraudulent operations, and a great degree of mixing between both. To ensure proper model construction, a nonlinear version of Fisher's discriminant analysis, which adequately separates a good proportion of fraudulent operations away from other closer to normal traffic, has been used. The system is fully operational and currently handles more than 12 million operations per year with very satisfactory results.

Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

Science.gov (United States)

Tam, Wing-Kin; Yang, Zhi

2018-05-01

Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

Neural - levelset shape detection segmentation of brain tumors in dynamic susceptibility contrast enhanced and diffusion weighted magnetic resonance images

International Nuclear Information System (INIS)

Vijayakumar, C.; Bhargava, Sunil; Gharpure, Damayanti Chandrashekhar

2008-01-01

A novel Neuro - level set shape detection algorithm is proposed and evaluated for segmentation and grading of brain tumours. The algorithm evaluates vascular and cellular information provided by dynamic contrast susceptibility magnetic resonance images and apparent diffusion coefficient maps. The proposed neural shape detection algorithm is based on the levels at algorithm (shape detection algorithm) and utilizes a neural block to provide the speed image for the level set methods. In this study, two different architectures of level set method have been implemented and their results are compared. The results show that the proposed Neuro-shape detection performs better in differentiating the tumor, edema, necrosis in reconstructed images of perfusion and diffusion weighted magnetic resonance images. (author)

Serum feline-specific pancreatic lipase immunoreactivity concentrations and abdominal ultrasonographic findings in cats with trauma resulting from high-rise syndrome.

Science.gov (United States)

Zimmermann, Elke; Hittmair, Katharina M; Suchodolski, Jan S; Steiner, Jörg M; Tichy, Alexander; Dupré, Gilles

2013-05-01

To evaluate serum feline-specific pancreatic lipase immunoreactivity (fPLI) concentrations and abdominal ultrasonographic findings in cats with trauma resulting from high-rise syndrome. Prospective case series. Animals-34 client-owned cats. From cats evaluated because of high-rise syndrome between March and October 2009, a blood sample was obtained for measurement of serum fPLI concentration within 12 hours after the fall and at 24, 48, and 72 hours after the first blood collection. Pancreatitis was diagnosed in cats with an fPLI concentration > 5.4 μg/L. Each cat had abdominal ultrasonography performed twice 48 hours apart, and pancreatic trauma was assessed via detection of pancreatic enlargement, hypoechoic or heteroechoic pancreatic parenchyma, hyperechoic mesentery, and peritoneal effusion. Cats were assigned 1 point for each abnormality present, and a cumulative score ≥ 3 was considered suggestive of traumatic pancreatitis. Traumatic pancreatitis was diagnosed in 9 and 8 cats on the basis of serum fPLI concentration and ultrasonographic findings, respectively. For cats with pancreatitis, fPLI concentration was significantly higher at 12 and 24 hours after the fall than at 48 and 72 hours after the fall, and serum fPLI concentration decreased as time after the fall increased. Significant agreement existed between the use of serum fPLI concentration and abdominal ultrasonography for the diagnosis of traumatic pancreatitis. Cats with high-rise syndrome often had serum fPLI concentrations > 5.4 μg/L within 12 hours after the fall, and concurrent evaluation of those cats via abdominal ultrasonography twice, 48 hours apart, improved detection of traumatic pancreatitis.

Credit card fraud detection using neural network and geolocation

Science.gov (United States)

Gulati, Aman; Dubey, Prakash; MdFuzail, C.; Norman, Jasmine; Mangayarkarasi, R.

2017-11-01

The most acknowledged payment mode is credit card for both disconnected and online mediums in today's day and age. It facilitates cashless shopping everywhere in the world. It is the most widespread and reasonable approach with regards to web based shopping, paying bills, what's more, performing other related errands. Thus danger of fraud exchanges utilizing credit card has likewise been expanding. In the Current Fraud Detection framework, false exchange is recognized after the transaction is completed. As opposed to the current system, the proposed system presents a methodology which facilitates the detection of fraudulent exchanges while they are being processed, this is achieved by means of Behaviour and Locational Analysis(Neural Logic) which considers a cardholder's way of managing money and spending pattern. A deviation from such a pattern will then lead to the system classifying it as suspicious transaction and will then be handled accordingly.

Automatic QRS complex detection using two-level convolutional neural network.

Science.gov (United States)

Xiang, Yande; Lin, Zhitao; Meng, Jianyi

2018-01-29

The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances. In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values. An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.

Digital mammography: Mixed feature neural network with spectral entropy decision for detection of microcalcifications

Energy Technology Data Exchange (ETDEWEB)

Zheng, B. [Univ. of South Florida, Tampa, FL (United States)]|[Nanjing Univ. of Posts and Telecommunications (China). Dept. of Telecommunication Engineering; Qian, W.; Clarke, L.P. [Univ. of South Florida, Tampa, FL (United States)

1996-10-01

A computationally efficient mixed feature based neural network (MFNN) is proposed for the detection of microcalcification clusters (MCC`s) in digitized mammograms. The MFNN employs features computed in both the spatial and spectral domain and uses spectral entropy as a decision parameter. Backpropagation with Kalman Filtering (KF) is employed to allow more efficient network training as required for evaluation of different features, input images, and related error analysis. A previously reported, wavelet-based image-enhancement method is also employed to enhance microcalcification clusters for improved detection. The relative performance of the MFNN for both the raw and enhanced images is evaluated using a common image database of 30 digitized mammograms, with 20 images containing 21 biopsy proven MCC`s and ten normal cases. The computed sensitivity (true positive (TP) detection rate) was 90.1% with an average low false positive (FP) detection of 0.71 MCCs/image for the enhanced images using a modified k-fold validation error estimation technique. The corresponding computed sensitivity for the raw images was reduced to 81.4% and with 0.59 FP`s MCCs/image. A relative comparison to an earlier neural network (NN) design, using only spatially related features, suggests the importance of the addition of spectral domain features when the raw image data are analyzed.

Digital mammography: Mixed feature neural network with spectral entropy decision for detection of microcalcifications

International Nuclear Information System (INIS)

Zheng, B.

1996-01-01

A computationally efficient mixed feature based neural network (MFNN) is proposed for the detection of microcalcification clusters (MCC's) in digitized mammograms. The MFNN employs features computed in both the spatial and spectral domain and uses spectral entropy as a decision parameter. Backpropagation with Kalman Filtering (KF) is employed to allow more efficient network training as required for evaluation of different features, input images, and related error analysis. A previously reported, wavelet-based image-enhancement method is also employed to enhance microcalcification clusters for improved detection. The relative performance of the MFNN for both the raw and enhanced images is evaluated using a common image database of 30 digitized mammograms, with 20 images containing 21 biopsy proven MCC's and ten normal cases. The computed sensitivity (true positive (TP) detection rate) was 90.1% with an average low false positive (FP) detection of 0.71 MCCs/image for the enhanced images using a modified k-fold validation error estimation technique. The corresponding computed sensitivity for the raw images was reduced to 81.4% and with 0.59 FP's MCCs/image. A relative comparison to an earlier neural network (NN) design, using only spatially related features, suggests the importance of the addition of spectral domain features when the raw image data are analyzed

Automatic detection of kidney in 3D pediatric ultrasound images using deep neural networks

Science.gov (United States)

Tabrizi, Pooneh R.; Mansoor, Awais; Biggs, Elijah; Jago, James; Linguraru, Marius George

2018-02-01

Ultrasound (US) imaging is the routine and safe diagnostic modality for detecting pediatric urology problems, such as hydronephrosis in the kidney. Hydronephrosis is the swelling of one or both kidneys because of the build-up of urine. Early detection of hydronephrosis can lead to a substantial improvement in kidney health outcomes. Generally, US imaging is a challenging modality for the evaluation of pediatric kidneys with different shape, size, and texture characteristics. The aim of this study is to present an automatic detection method to help kidney analysis in pediatric 3DUS images. The method localizes the kidney based on its minimum volume oriented bounding box) using deep neural networks. Separate deep neural networks are trained to estimate the kidney position, orientation, and scale, making the method computationally efficient by avoiding full parameter training. The performance of the method was evaluated using a dataset of 45 kidneys (18 normal and 27 diseased kidneys diagnosed with hydronephrosis) through the leave-one-out cross validation method. Quantitative results show the proposed detection method could extract the kidney position, orientation, and scale ratio with root mean square values of 1.3 +/- 0.9 mm, 6.34 +/- 4.32 degrees, and 1.73 +/- 0.04, respectively. This method could be helpful in automating kidney segmentation for routine clinical evaluation.

Power plant fault detection using artificial neural network

Science.gov (United States)

Thanakodi, Suresh; Nazar, Nazatul Shiema Moh; Joini, Nur Fazriana; Hidzir, Hidzrin Dayana Mohd; Awira, Mohammad Zulfikar Khairul

2018-02-01

The fault that commonly occurs in power plants is due to various factors that affect the system outage. There are many types of faults in power plants such as single line to ground fault, double line to ground fault, and line to line fault. The primary aim of this paper is to diagnose the fault in 14 buses power plants by using an Artificial Neural Network (ANN). The Multilayered Perceptron Network (MLP) that detection trained utilized the offline training methods such as Gradient Descent Backpropagation (GDBP), Levenberg-Marquardt (LM), and Bayesian Regularization (BR). The best method is used to build the Graphical User Interface (GUI). The modelling of 14 buses power plant, network training, and GUI used the MATLAB software.

Construction and application of hierarchical decision tree for classification of ultrasonographic prostate images

NARCIS (Netherlands)

Giesen, R. J.; Huynen, A. L.; Aarnink, R. G.; de la Rosette, J. J.; Debruyne, F. M.; Wijkstra, H.

1996-01-01

A non-parametric algorithm is described for the construction of a binary decision tree classifier. This tree is used to correlate textural features, computed from ultrasonographic prostate images, with the histopathology of the imaged tissue. The algorithm consists of two parts; growing and pruning.

Combined ultrasonographically guided drainage and laparoscopic excision of a large ovarian cyst.

Science.gov (United States)

Nagele, F; Magos, A L

1996-11-01

Large ovarian cysts are conventionally treated by laparotomy. We describe a technique of transabdominal drainage under ultrasonographic control followed by laparoscopic excision of an ovarian cyst that was 24 x 10 x 20 cm. This approach has the benefits of minimal-access surgery and is suitable for unilocular benign cysts of any size.

Ultrasonographic findings of uterine polypoid adenomyomas

Energy Technology Data Exchange (ETDEWEB)

Lee, Eun Ju [Ajou University School of Medicine, Suwon (Korea, Republic of)

2002-12-15

To characterize the ultrasonographic findings of polypoid adenomyoma of the uterus. Ultrasonographic findings of twenty seven patients with histologically confirmed polypoid adenomyoma were retrospectively reviewed. Ultrasonography (US) was performed in all patients while sonohysterography (SH) in fifteen patients and color Doppler sonography (CDS) in thirteen patients were additionally performed. Location, size, growth pattern, surface, margin from the endometrim and underlying myometrium, echogenecity and echotexture, presence and patterns of cystic areas, hemorrhage, and posterior shadowing of the endometrial or submucosal mass on US and SH were evaluated. The presence of blood flow and resistive index (RI) on CDS were also evaluated. On US and SH, the tumor location was the corpus in sixteen cases, fundus in eight, and isthmus in three cases, and the tumor size ranged from 0.5 to 6 cm (mean 3.5 cm). The tumors were polypoid in eighteen cases, sessile in four cases, and pedunculated in five cases, and three of them protruded into endocervical canal while two cases prolapsed through externals os. The surface was smooth in twenty six cases, lobulated in four and irregular in one. Nineteen cases had ill defined margin while eight cases, a well circumscribed margin. The mass was inhomogeneously isoechoic in twelve cases, homogeneously isoechoic in seven cases, homogeneously and inhomogeneously hyperechoic in four cases each, respectively. Cystic areas were seen in twenty cases, and there were three patterns of cystic areas: all solid mass (pattern 1, n=7), solid mass with cystic areas (pattern 2, n=18) and predominantly cystic mass (pattern 3, n=2). Eight cases had hemorrhage and seven had posterior shadowing. CDS showed a blood flow with range of RI from 0.19 to 0.74 (mean 0.47). Other findings included adenomyosis in sixteen cases, leiomyoma in three, and endometrial thickening and mass in one each, respectively. Polypoid adenomyoma can be characterized as a

[Interest of ultrasonographic guidance in paediatric regional anaesthesia].

Science.gov (United States)

Dadure, C; Raux, O; Rochette, A; Capdevila, X

2009-10-01

The use of ultrasonographic guidance for regional anaesthesia has known recently a big interest in children in recent years. The linear ultrasound probes with a 25 mm active surface area (or probes with 38 mm active surface area in older children), with high sound frequencies in the range 8-14 MHz, allow a good compromise between excellent resolution for superficial structure and good penetration depths. In children, the easiest ultrasound guided blocks are axillar blocks, femoral blocks, fascia iliaca compartment blocks, ilio-inguinal blocks and para-umbilical blocks, caudal blocks. They permit a safe and easy learning curve of these techniques. The main advantage of ultrasound guided regional anaesthesia is the visualization of different anatomical structures and the approximate localization of the tip of needle. The other advantages for ultrasound guided peripheral nerve blocks in children are: faster onset time of sensory and motor block, longer duration of sensory blockade, increase of blockade quality and reduction of local anesthetic injection. The use of ultrasonographic guidance for central block allows to visualize different structures as well as spine and his content. Spinous process, ligament flavum, dura mater, conus medullaris and cerebrospinal fluid are identifiable, and give some information on spine, epidural space and the depth between epidural space and skin. At last, in caudal block, ultrasounds permit to evaluate the anatomy of caudal epidural space, especially the relation of the sacral hiatus to the dural sac and the search of occult spinal dysraphism. Benefit of this technique is the visualization of targeted nerves or spaces and the spread of injected local anaesthetic.

Clinical and ultrasonographic observations of functional and mechanical intestinal obstruction in buffaloes (Bubalus bubalis

Directory of Open Access Journals (Sweden)

Arafat Khalphallah

2016-05-01

Full Text Available Aim: This study was designed for clinical and laboratory evaluation of intestinal obstruction (IO in buffaloes (Bubalus bubalis with special emphasis on the diagnostic value of ultrasonographic findings. Materials and Methods: A total number of 30 buffaloes were included in the study and divided into 2 groups: Healthy (n=10 and diseased group (n=20. Diseased buffaloes were admitted to the Veterinary Teaching Hospital at Assiut University, Egypt, with a history of anorexia, abdominal pain, various degrees of abdominal distention, and absence or presence of scanty mucoid faces. These animals were subjected to clinical and ultrasonographic as well as laboratory examinations. Results: Based on ultrasonographic findings, various forms of IO were diagnosed. Functional obstruction, paralytic ileus, was diagnosed in 17 cases (85% while mechanical IO was diagnosed only in 3 cases (15%. Out of 17 cases of paralytic ileus, both proximal and distal ileuses were successfully imaged in 8 and 9 cases, respectively. Proximal ileus was imaged from the right dorsal flank region as a single dilated loop of diameter >6 cm, while distal ileus was imaged as multiple dilated loops of diameter <6 cm. Mechanical obstruction due to duodenal intussusception was visualized as two concentric rings with outer echogenic wall and hypoechoic lumen. All cases of IO showed leukocytosis, hypoproteinemia, and increased activity of alkaline phosphatase and aspartate aminotransferase. Conclusion: Ultrasonography proved to be an essential tool for diagnosis and differential diagnosis of various forms of IO in buffaloes.

Premature ventricular contraction detection combining deep neural networks and rules inference.

Science.gov (United States)

Zhou, Fei-Yan; Jin, Lin-Peng; Dong, Jun

2017-06-01

Premature ventricular contraction (PVC), which is a common form of cardiac arrhythmia caused by ectopic heartbeat, can lead to life-threatening cardiac conditions. Computer-aided PVC detection is of considerable importance in medical centers or outpatient ECG rooms. In this paper, we proposed a new approach that combined deep neural networks and rules inference for PVC detection. The detection performance and generalization were studied using publicly available databases: the MIT-BIH arrhythmia database (MIT-BIH-AR) and the Chinese Cardiovascular Disease Database (CCDD). The PVC detection accuracy on the MIT-BIH-AR database was 99.41%, with a sensitivity and specificity of 97.59% and 99.54%, respectively, which were better than the results from other existing methods. To test the generalization capability, the detection performance was also evaluated on the CCDD. The effectiveness of the proposed method was confirmed by the accuracy (98.03%), sensitivity (96.42%) and specificity (98.06%) with the dataset over 140,000 ECG recordings of the CCDD. Copyright © 2017 Elsevier B.V. All rights reserved.

Clinical, ultrasonographic, and roentgenographic study in 134 asymptomatic gallstone carriers

International Nuclear Information System (INIS)

Lirussi, F.; Passera, D.; Iemmolo, R.M.; Nassuato, G.; Okolicsanyi, L.

1993-01-01

The authors investigated retrospectively the ultrasonographic and roentgenographic characteristics of the gallstones and the gallbladder in 134 symtom-free carriers and evaluated prospectively the outcome and side effects of 6 to 24 months' ursodeoxycholic acid (UDCA) therapy in 36 individuals with silent stones. Two-thirds of the 134 subjects had multiple stones, and 71 to 75% had stones less than 15 mm in diameter. Gallstone calcification was detected in 13%. A non-functioning gallbladder was observed in 19%, whereas gallbladder contraction was normal in 64 of 76 gallstone carriers. With regard to oral bile acid treatment, complete and partial dissolutions were achieved in 7 and 9 of 33 subjects, respectively (48.5%). Development of a non-functioning gallbladder occurred in 9%, and acquired gallstone calcification was seen in another 15%. It is concluded that: i) the characteristics of the gallstones and the gallbladder are similar to those observed in symptomatic patients, and ii) UDCA therapy may be given in selected symptom-free carriers for no more than 6 to 12 months. Thereafter, it does not appear to be cost-effective. 23 refs., 2 figs., 3 tabs

RADIOGRAPHIC AND ULTRASONOGRAPHIC ABDOMINAL ANATOMY IN CAPTIVE RING-TAILED LEMURS (LEMUR CATTA).

Science.gov (United States)

Makungu, Modesta; du Plessis, Wencke M; Barrows, Michelle; Groenewald, Hermanus B; Koeppel, Katja N

2016-06-01

The ring-tailed lemur (Lemur catta) is primarily distributed in south and southwestern Madagascar. It is classified as an endangered species by the International Union for Conservation of Nature. Various abdominal diseases, such as hepatic lipidosis, intestinal ulcers, cystitis, urinary tract obstruction, and neoplasia (e.g., colonic adenocarcinoma and cholangiocarcinoma), have been reported in this species. The aim of this study was to describe the normal radiographic and ultrasonographic abdominal anatomy in captive ring-tailed lemurs to provide guidance for clinical use. Radiography of the abdomen and ultrasonography of the liver, spleen, kidneys, and urinary bladder were performed in 13 and 9 healthy captive ring-tailed lemurs, respectively, during their annual health examinations. Normal radiographic and ultrasonographic reference ranges for abdominal organs were established and ratios were calculated. The majority (12/13) of animals had seven lumbar vertebrae. The sacrum had mainly (12/13) three segments. Abdominal serosal detail was excellent in all animals, and hypaxial muscles were conspicuous in the majority (11/13) of animals. The spleen was frequently (12/13) seen on the ventrodorsal (VD) view and rarely (3/13) on the right lateral (RL) view. The liver was less prominent and well contained within the ribcage. The pylorus was mostly (11/13) located to the right of the midline. The right and left kidneys were visible on the RL and VD views, with the right kidney positioned more cranial and dorsal to the left kidney. On ultrasonography, the kidneys appeared ovoid on transverse and longitudinal views. The medulla was hypoechoic to the renal cortex. The renal cortex was frequently (8/9) isoechoic and rarely (1/9) hyperechoic to the splenic parenchyma. The liver parenchyma was hypoechoic (5/5) to the renal cortex. Knowledge of the normal radiographic and ultrasonographic abdominal anatomy of ring-tailed lemurs may be useful in the diagnosis of diseases and in

Subacute granulomatous (De Quervain′s thyroiditis: Fine-needle aspiration cytology and ultrasonographic characteristics of 21 cases

Directory of Open Access Journals (Sweden)

Çigdem Vural

2015-01-01

Full Text Available Background: Subacute granulomatous thyroiditis (SGT is an inflammatory disease that presents with different clinical and cytological characteristics. Although the diagnosis is generally made clinically, imaging methods and fine-needle aspiration (FNA may provide assistance, particularly in atypical cases. The objective of this study is to reveal the ultrasonographic (USG and cytological characteristics of SGT. Materials and Methods: The clinical, USG and cytological findings of 21 cases diagnosed with SGT were reviewed. Results: Ultrasonographic data was available in 20 cases. A hypoechoic thyroid nodule with irregular margins was detected in 12 of the 20 total cases. Of these, 9 cases complained about pain in the thyroid lodge and generally had unilateral lesions, heterogeneous and hypoechoic areas with indistinct margins, rather than nodular lesions, which were seen in 7 cases. Cytologically, the multinuclear giant cells (MNGCs found in all cases were accompanied by a dirty background containing varying numbers of granulomatous structures, including isolated epithelioid histiocytes, proliferated/regenerated follicle epithelium cells and inflammatory cells and colloid. Conclusion: Though hypoechoic and heterogeneous areas with irregular margins are strongly associated with thyroiditis, SGT may also appear as painful or painless hypoechoic, solid nodules and generate challenges in differential diagnosis. Although the most remarkable characteristic observed in FNA cytology was the presence of multiple MNGCs with cytoplasm, a dirty background accompanied by mild-moderate cellularity, degenerated-proliferated follicular epithelium cells, rare epithelioid granulomas and mixed type inflammatory cells are characteristic for SGT. The assessment of these radiological and cytological findings in conjunction with clinical findings will assist in the achievement of an accurate diagnosis.

Diagnosis and ultrasonographic appearance of hepatic metastasis in six cases of canine appendicular osteosarcoma (2005-2013).

Science.gov (United States)

Cesario, L; Garrett, L D; Barger, A M; O'Brien, R T; Fan, T M

2016-05-01

The aims of this retrospective study were to identify clinical cases of dogs with appendicular osteosarcoma (OSA) in which hepatic metastasis was confirmed, to highlight the use of cytology for its diagnosis and to describe the radiographic and ultrasonographic appearances of the lesion. Medical records were retrospectively reviewed for dogs with appendicular OSA and hepatic metastases between January 2005 and January 2013. Reviews of radiographs, ultrasounds and cytology were performed. Six dogs with appendicular OSA and hepatic metastases were identified. The ultrasonographic appearance of metastatic lesions varied, including hyperechoic with shadowing, hyperechoic without shadowing, hypoechoic and mixed echogenicity. In two cases, the hepatic metastases were also evident on thoracic radiographs. The mean survival time from diagnosis of appendicular OSA was 188 days (range 69-363 days) and from diagnosis of hepatic metastases was 35 days (range 2-69 days). Death was tumour-related in all cases. Hepatic metastasis varies widely in its ultrasonographic appearance. In three of six cases, hepatic metastasis was identified without concurrent pulmonary metastasis; therefore, abdominal ultrasound may be useful at regular intervals for patient evaluation, especially in clinical trials where accurate identification of the disease-free interval is crucial. Once hepatic metastasis is confirmed, survival times appear limited. © 2016 Australian Veterinary Association.

Pitfall of ultrasonographic diagnosis in abdominal tuberculosis

International Nuclear Information System (INIS)

Lee, Y. H.; Yoo, H.S.; Kim, K. W.; Lee, J. T.; Park, C. Y.

1983-01-01

Intestinal tuberculosis is generally diagnosed using conventional barium studies, however recent diagnostic modalities such as ultrasonography and CT scan are widely applicated in conjunction with conventional studies for the search of lymph node presentation and associated extra-intestinal organs. It is important to differentiate intra-abdominal tuberculosis from metastatic or lymphomatous disease clinically. And it might be especially of worth to find out if there is any differential point between tuberculosis and other lymph nodal disease entities when we meet similar findings on imaging modalities. Authors have tried to evaluate ultrasonographic findings in conjunction with other studies in nine cases of abdominal tuberculosis which showed mainly extra-intestinal and/or lymph nodal involvement

Foot Disability in Patients with Ankylosing Spondylitis: A Clinical and Ultrasonographic Assessment

Directory of Open Access Journals (Sweden)

Erkan Mesci

2016-04-01

Full Text Available Aim: The objective of this study was to perform a clinical and ultrasonographic assessment of foot disability and related factors among patients with ankylosing spondylitis. Material and Method: The study enrolled 40 patients diagnosed with ankylosing spondylitis (AS according to the modified New York criteria and 30 matched healthy controls. In addition to the assessments for Disease activity (BASDAI and functional status (BASFI, foot functioning was evaluated using the Foot Function Index (FFI and quality of life using the Ankylosing Spondylitis Quality of Life (ASQoL questionnaire. Thickness of plantar fascia (PF and Achilles tendon (AT, changes in echogenicity and presence of bone erosions, entesophytes and bursitis were examined using ultrasound. Results: The mean age of patients was 39.9 ± 10.4 years and median disease duration was 48 (1-288 months. Sixteen patients (40% had foot pain. Thirteen patients (32.5% had clinical evidence for enthesitis. Thirty patients (75% showed at least one pathological finding at ultrasonographic examination. Mean FFI score was higher in the AS group versus control group (p

Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode

Directory of Open Access Journals (Sweden)

Tao Ye

2018-06-01

Full Text Available Many accidents happen under shunting mode when the speed of a train is below 45 km/h. In this mode, train attendants observe the railway condition ahead using the traditional manual method and tell the observation results to the driver in order to avoid danger. To address this problem, an automatic object detection system based on convolutional neural network (CNN is proposed to detect objects ahead in shunting mode, which is called Feature Fusion Refine neural network (FR-Net. It consists of three connected modules, i.e., the depthwise-pointwise convolution, the coarse detection module, and the object detection module. Depth-wise-pointwise convolutions are used to improve the detection in real time. The coarse detection module coarsely refine the locations and sizes of prior anchors to provide better initialization for the subsequent module and also reduces search space for the classification, whereas the object detection module aims to regress accurate object locations and predict the class labels for the prior anchors. The experimental results on the railway traffic dataset show that FR-Net achieves 0.8953 mAP with 72.3 FPS performance on a machine with a GeForce GTX1080Ti with the input size of 320 × 320 pixels. The results imply that FR-Net takes a good tradeoff both on effectiveness and real time performance. The proposed method can meet the needs of practical application in shunting mode.

Determining the effectiveness of various treatment modalities in carpal tunnel syndrome by ultrasonography and comparing ultrasonographic findings with other outcomes.

Science.gov (United States)

Soyupek, Feray; Yesildag, Ahmet; Kutluhan, Suleyman; Askin, Ayhan; Ozden, Ahmet; Uslusoy, Gokcen Ay; Demirci, Seden

2012-10-01

Firstly, we aimed to determine the effectiveness of various treatment modalities using ultrasonography (US), and secondly, we aimed to assess the correlations between the ultrasonographic findings and electrophysiological tests, symptom severity, functional status and physical findings. 74 hands of 47 patients with carpal tunnel syndrome (CTS) were randomly treated by applying wrist splinting alone in the neutral position (23 hands), phonophoresis with corticosteroid (PCS) (28 hands) and phonophoresis with non-steroid anti-inflamatory drug (PNSAI) (23 hands). The cross-sectional area (CSA) of the median nerve (MN) was determined by ultrasound on the initial and at the 3 months after treatment. MN conduction studies were performed on the initial visit and 3 months after treatment. The patients completed the Boston symptom severity questionnaire. For clinical evaluation, we used Phalen's and Tinel's signs. We could find reduction in CSA of MN in PCS group (P 0.05) and also between ultrasonographic parameters and BQ scores (P > 0.05). Although there was some improvement in clinical parameters, ultrasonographic parameters did not change in P-NSAI group. The most effective treatment modality was P-CS according to ultrasonographic and other findings. Although there were inverse correlations between the CSA of MN and sensory and motor MN conduction velocity, no relationship was found between symptom severity, functional status and US findings or electrophysiological studies.

Ultrasonographic Findings of Breast Abscess

Energy Technology Data Exchange (ETDEWEB)

Shin, Hyeong Cheol; Oh, Ki Keun [Yonsei University College of Medicine, Seoul (Korea, Republic of)

1995-06-15

Breast abscess cannot be differentiated from breast malignancy by film mammography. Pain and spread of infection can be developed during film mammography procedure due to compression. However, ultrasonography is known to be an adequate procedure for diagnosis of breast abscesses. Therefore, we performed the present study to document the ultrasonographic findings of breast abscess. We analyzed ultrasonograms of ninexases with surgically proven breast abscesses. All patients were female and their ages ranged from l2 to 56 years(average, 35 years). The lesion was located in the right breast in four cases, and in the left in five cases. On ultrasonography, all lesions were anechoic or low echoic. The lesion showed mixed echogenicityin five cases. Posterior acoustic enhancement was noted in seven cases. Lateral shadowing was seen in four cases.There were skin thickening in five cases and subcutaneous fat obliteration in all cases. Ultrasonography is useful in the diagnosis of breast abscess

Ultrasonographic Findings of Breast Abscess

International Nuclear Information System (INIS)

Shin, Hyeong Cheol; Oh, Ki Keun

1995-01-01

Breast abscess cannot be differentiated from breast malignancy by film mammography. Pain and spread of infection can be developed during film mammography procedure due to compression. However, ultrasonography is known to be an adequate procedure for diagnosis of breast abscesses. Therefore, we performed the present study to document the ultrasonographic findings of breast abscess. We analyzed ultrasonograms of ninexases with surgically proven breast abscesses. All patients were female and their ages ranged from l2 to 56 years(average, 35 years). The lesion was located in the right breast in four cases, and in the left in five cases. On ultrasonography, all lesions were anechoic or low echoic. The lesion showed mixed echogenicityin five cases. Posterior acoustic enhancement was noted in seven cases. Lateral shadowing was seen in four cases.There were skin thickening in five cases and subcutaneous fat obliteration in all cases. Ultrasonography is useful in the diagnosis of breast abscess

Model for Detection and Classification of DDoS Traffic Based on Artificial Neural Network

Directory of Open Access Journals (Sweden)

D. Peraković

2017-06-01

Full Text Available Detection of DDoS (Distributed Denial of Service traffic is of great importance for the availability protection of services and other information and communication resources. The research presented in this paper shows the application of artificial neural networks in the development of detection and classification model for three types of DDoS attacks and legitimate network traffic. Simulation results of developed model showed accuracy of 95.6% in classification of pre-defined classes of traffic.

A Hypergraph and Arithmetic Residue-based Probabilistic Neural Network for classification in Intrusion Detection Systems.

Science.gov (United States)

Raman, M R Gauthama; Somu, Nivethitha; Kirthivasan, Kannan; Sriram, V S Shankar

2017-08-01

Over the past few decades, the design of an intelligent Intrusion Detection System (IDS) remains an open challenge to the research community. Continuous efforts by the researchers have resulted in the development of several learning models based on Artificial Neural Network (ANN) to improve the performance of the IDSs. However, there exists a tradeoff with respect to the stability of ANN architecture and the detection rate for less frequent attacks. This paper presents a novel approach based on Helly property of Hypergraph and Arithmetic Residue-based Probabilistic Neural Network (HG AR-PNN) to address the classification problem in IDS. The Helly property of Hypergraph was exploited for the identification of the optimal feature subset and the arithmetic residue of the optimal feature subset was used to train the PNN. The performance of HG AR-PNN was evaluated using KDD CUP 1999 intrusion dataset. Experimental results prove the dominance of HG AR-PNN classifier over the existing classifiers with respect to the stability and improved detection rate for less frequent attacks. Copyright © 2017 Elsevier Ltd. All rights reserved.

Fuzzy-neural network in the automatic detection and volumetry of the spleen on spiral CT scans

International Nuclear Information System (INIS)

Heitmann, K.R.; Mainz Univ.; Rueckert, S.; Heussel, C.P.; Thelen, M.; Kauczor, H.U.; Uthmann, T.

2000-01-01

Purpose: To assess spleen segmentation and volumetry in spiral CT scans with and without pathological changes of splenic tissue. Methods: The image analysis software HYBRIKON is based on region growing, self-organized neural nets, and fuzzy-anatomic rules. The neural nets were trained with spiral CT data from 10 patients, not used in the following evaluation on spiral CT scans from 19 patients. An experienced radiologist verified the results. The true positive and false positive areas were compared in terms to the areas marked by the radiologist. The results were compared with a standard thresholding method. Results: The neural nets achieved a higher accuracy than the thresholding method. Correlation coefficient of the fuzzy-neural nets: 0.99 (thresholding: 0.63). Mean true positive rate: 90% (thresholding: 75%), mean false positive rate: 5% (thresholding>100%). Pitfalls were caused by accessory spleens, extreme changes in the morphology (tumors, metastases, cysts), and parasplenic masses. Conclusions: Self-organizing neural nets combined with fuzzy rules are ready for use in the automatic detection and volumetry of the spleen in spiral CT scans. (orig.) [de

Neural Cell Chip Based Electrochemical Detection of Nanotoxicity.

Science.gov (United States)

Kafi, Md Abdul; Cho, Hyeon-Yeol; Choi, Jeong Woo

2015-07-02

Development of a rapid, sensitive and cost-effective method for toxicity assessment of commonly used nanoparticles is urgently needed for the sustainable development of nanotechnology. A neural cell with high sensitivity and conductivity has become a potential candidate for a cell chip to investigate toxicity of environmental influences. A neural cell immobilized on a conductive surface has become a potential tool for the assessment of nanotoxicity based on electrochemical methods. The effective electrochemical monitoring largely depends on the adequate attachment of a neural cell on the chip surfaces. Recently, establishment of integrin receptor specific ligand molecules arginine-glycine-aspartic acid (RGD) or its several modifications RGD-Multi Armed Peptide terminated with cysteine (RGD-MAP-C), C(RGD)₄ ensure farm attachment of neural cell on the electrode surfaces either in their two dimensional (dot) or three dimensional (rod or pillar) like nano-scale arrangement. A three dimensional RGD modified electrode surface has been proven to be more suitable for cell adhesion, proliferation, differentiation as well as electrochemical measurement. This review discusses fabrication as well as electrochemical measurements of neural cell chip with particular emphasis on their use for nanotoxicity assessments sequentially since inception to date. Successful monitoring of quantum dot (QD), graphene oxide (GO) and cosmetic compound toxicity using the newly developed neural cell chip were discussed here as a case study. This review recommended that a neural cell chip established on a nanostructured ligand modified conductive surface can be a potential tool for the toxicity assessments of newly developed nanomaterials prior to their use on biology or biomedical technologies.

Neural Cell Chip Based Electrochemical Detection of Nanotoxicity

Directory of Open Access Journals (Sweden)

Md. Abdul Kafi

2015-07-01

Full Text Available Development of a rapid, sensitive and cost-effective method for toxicity assessment of commonly used nanoparticles is urgently needed for the sustainable development of nanotechnology. A neural cell with high sensitivity and conductivity has become a potential candidate for a cell chip to investigate toxicity of environmental influences. A neural cell immobilized on a conductive surface has become a potential tool for the assessment of nanotoxicity based on electrochemical methods. The effective electrochemical monitoring largely depends on the adequate attachment of a neural cell on the chip surfaces. Recently, establishment of integrin receptor specific ligand molecules arginine-glycine-aspartic acid (RGD or its several modifications RGD-Multi Armed Peptide terminated with cysteine (RGD-MAP-C, C(RGD4 ensure farm attachment of neural cell on the electrode surfaces either in their two dimensional (dot or three dimensional (rod or pillar like nano-scale arrangement. A three dimensional RGD modified electrode surface has been proven to be more suitable for cell adhesion, proliferation, differentiation as well as electrochemical measurement. This review discusses fabrication as well as electrochemical measurements of neural cell chip with particular emphasis on their use for nanotoxicity assessments sequentially since inception to date. Successful monitoring of quantum dot (QD, graphene oxide (GO and cosmetic compound toxicity using the newly developed neural cell chip were discussed here as a case study. This review recommended that a neural cell chip established on a nanostructured ligand modified conductive surface can be a potential tool for the toxicity assessments of newly developed nanomaterials prior to their use on biology or biomedical technologies.

Small-scale anomaly detection in panoramic imaging using neural models of low-level vision

Science.gov (United States)

Casey, Matthew C.; Hickman, Duncan L.; Pavlou, Athanasios; Sadler, James R. E.

2011-06-01

Our understanding of sensory processing in animals has reached the stage where we can exploit neurobiological principles in commercial systems. In human vision, one brain structure that offers insight into how we might detect anomalies in real-time imaging is the superior colliculus (SC). The SC is a small structure that rapidly orients our eyes to a movement, sound or touch that it detects, even when the stimulus may be on a small-scale; think of a camouflaged movement or the rustle of leaves. This automatic orientation allows us to prioritize the use of our eyes to raise awareness of a potential threat, such as a predator approaching stealthily. In this paper we describe the application of a neural network model of the SC to the detection of anomalies in panoramic imaging. The neural approach consists of a mosaic of topographic maps that are each trained using competitive Hebbian learning to rapidly detect image features of a pre-defined shape and scale. What makes this approach interesting is the ability of the competition between neurons to automatically filter noise, yet with the capability of generalizing the desired shape and scale. We will present the results of this technique applied to the real-time detection of obscured targets in visible-band panoramic CCTV images. Using background subtraction to highlight potential movement, the technique is able to correctly identify targets which span as little as 3 pixels wide while filtering small-scale noise.

Automated sleep stage detection with a classical and a neural learning algorithm--methodological aspects.

Science.gov (United States)

Schwaibold, M; Schöchlin, J; Bolz, A

2002-01-01

For classification tasks in biosignal processing, several strategies and algorithms can be used. Knowledge-based systems allow prior knowledge about the decision process to be integrated, both by the developer and by self-learning capabilities. For the classification stages in a sleep stage detection framework, three inference strategies were compared regarding their specific strengths: a classical signal processing approach, artificial neural networks and neuro-fuzzy systems. Methodological aspects were assessed to attain optimum performance and maximum transparency for the user. Due to their effective and robust learning behavior, artificial neural networks could be recommended for pattern recognition, while neuro-fuzzy systems performed best for the processing of contextual information.

A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks.

Science.gov (United States)

Alauthaman, Mohammad; Aslam, Nauman; Zhang, Li; Alasem, Rafe; Hossain, M A

2018-01-01

In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed.

Efficient Cancer Detection Using Multiple Neural Networks.

Science.gov (United States)

Shell, John; Gregory, William D

2017-01-01

The inspection of live excised tissue specimens to ascertain malignancy is a challenging task in dermatopathology and generally in histopathology. We introduce a portable desktop prototype device that provides highly accurate neural network classification of malignant and benign tissue. The handheld device collects 47 impedance data samples from 1 Hz to 32 MHz via tetrapolar blackened platinum electrodes. The data analysis was implemented with six different backpropagation neural networks (BNN). A data set consisting of 180 malignant and 180 benign breast tissue data files in an approved IRB study at the Aurora Medical Center, Milwaukee, WI, USA, were utilized as a neural network input. The BNN structure consisted of a multi-tiered consensus approach autonomously selecting four of six neural networks to determine a malignant or benign classification. The BNN analysis was then compared with the histology results with consistent sensitivity of 100% and a specificity of 100%. This implementation successfully relied solely on statistical variation between the benign and malignant impedance data and intricate neural network configuration. This device and BNN implementation provides a novel approach that could be a valuable tool to augment current medical practice assessment of the health of breast, squamous, and basal cell carcinoma and other excised tissue without requisite tissue specimen expertise. It has the potential to provide clinical management personnel with a fast non-invasive accurate assessment of biopsied or sectioned excised tissue in various clinical settings.

Detection and Localization of Robotic Tools in Robot-Assisted Surgery Videos Using Deep Neural Networks for Region Proposal and Detection.

Science.gov (United States)

Sarikaya, Duygu; Corso, Jason J; Guru, Khurshid A

2017-07-01

Video understanding of robot-assisted surgery (RAS) videos is an active research area. Modeling the gestures and skill level of surgeons presents an interesting problem. The insights drawn may be applied in effective skill acquisition, objective skill assessment, real-time feedback, and human-robot collaborative surgeries. We propose a solution to the tool detection and localization open problem in RAS video understanding, using a strictly computer vision approach and the recent advances of deep learning. We propose an architecture using multimodal convolutional neural networks for fast detection and localization of tools in RAS videos. To the best of our knowledge, this approach will be the first to incorporate deep neural networks for tool detection and localization in RAS videos. Our architecture applies a region proposal network (RPN) and a multimodal two stream convolutional network for object detection to jointly predict objectness and localization on a fusion of image and temporal motion cues. Our results with an average precision of 91% and a mean computation time of 0.1 s per test frame detection indicate that our study is superior to conventionally used methods for medical imaging while also emphasizing the benefits of using RPN for precision and efficiency. We also introduce a new data set, ATLAS Dione, for RAS video understanding. Our data set provides video data of ten surgeons from Roswell Park Cancer Institute, Buffalo, NY, USA, performing six different surgical tasks on the daVinci Surgical System (dVSS) with annotations of robotic tools per frame.

Change detection in multitemporal synthetic aperture radar images using dual-channel convolutional neural network

Science.gov (United States)

Liu, Tao; Li, Ying; Cao, Ying; Shen, Qiang

2017-10-01

This paper proposes a model of dual-channel convolutional neural network (CNN) that is designed for change detection in SAR images, in an effort to acquire higher detection accuracy and lower misclassification rate. This network model contains two parallel CNN channels, which can extract deep features from two multitemporal SAR images. For comparison and validation, the proposed method is tested along with other change detection algorithms on both simulated SAR images and real-world SAR images captured by different sensors. The experimental results demonstrate that the presented method outperforms the state-of-the-art techniques by a considerable margin.

Ship detection in optical remote sensing images based on deep convolutional neural networks

Science.gov (United States)

Yao, Yuan; Jiang, Zhiguo; Zhang, Haopeng; Zhao, Danpei; Cai, Bowen

2017-10-01

Automatic ship detection in optical remote sensing images has attracted wide attention for its broad applications. Major challenges for this task include the interference of cloud, wave, wake, and the high computational expenses. We propose a fast and robust ship detection algorithm to solve these issues. The framework for ship detection is designed based on deep convolutional neural networks (CNNs), which provide the accurate locations of ship targets in an efficient way. First, the deep CNN is designed to extract features. Then, a region proposal network (RPN) is applied to discriminate ship targets and regress the detection bounding boxes, in which the anchors are designed by intrinsic shape of ship targets. Experimental results on numerous panchromatic images demonstrate that, in comparison with other state-of-the-art ship detection methods, our method is more efficient and achieves higher detection accuracy and more precise bounding boxes in different complex backgrounds.

Biochemical and ultrasonographic predictors of outcome in threatened abortion

Directory of Open Access Journals (Sweden)

Ahmed M. Maged

2013-09-01

Conclusion: CA125, β HCG and progesterone are good biochemical markers and FHR and CRL are good ultrasonographic markers for the prediction of outcome in women with threatened abortion. FHR at 110 bpm gives the best predictivity followed by serum P at 25 ng/ml, β HCG at 19887 mIU/ml, CA 125 at 80 IU/ml and CRL at 21 mm with the least predictive accuracy among studied markers. Adding serum progesterone to FHR gave a sensitivity and specificity of 100%.

Global Detection of Live Virtual Machine Migration Based on Cellular Neural Networks

Directory of Open Access Journals (Sweden)

Kang Xie

2014-01-01

Full Text Available In order to meet the demands of operation monitoring of large scale, autoscaling, and heterogeneous virtual resources in the existing cloud computing, a new method of live virtual machine (VM migration detection algorithm based on the cellular neural networks (CNNs, is presented. Through analyzing the detection process, the parameter relationship of CNN is mapped as an optimization problem, in which improved particle swarm optimization algorithm based on bubble sort is used to solve the problem. Experimental results demonstrate that the proposed method can display the VM migration processing intuitively. Compared with the best fit heuristic algorithm, this approach reduces the processing time, and emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI implementation allowing the VM migration detection to be performed better.

Global detection of live virtual machine migration based on cellular neural networks.

Science.gov (United States)

Xie, Kang; Yang, Yixian; Zhang, Ling; Jing, Maohua; Xin, Yang; Li, Zhongxian

2014-01-01

In order to meet the demands of operation monitoring of large scale, autoscaling, and heterogeneous virtual resources in the existing cloud computing, a new method of live virtual machine (VM) migration detection algorithm based on the cellular neural networks (CNNs), is presented. Through analyzing the detection process, the parameter relationship of CNN is mapped as an optimization problem, in which improved particle swarm optimization algorithm based on bubble sort is used to solve the problem. Experimental results demonstrate that the proposed method can display the VM migration processing intuitively. Compared with the best fit heuristic algorithm, this approach reduces the processing time, and emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI) implementation allowing the VM migration detection to be performed better.

Automatic temporal segment detection via bilateral long short-term memory recurrent neural networks

Science.gov (United States)

Sun, Bo; Cao, Siming; He, Jun; Yu, Lejun; Li, Liandong

2017-03-01

Constrained by the physiology, the temporal factors associated with human behavior, irrespective of facial movement or body gesture, are described by four phases: neutral, onset, apex, and offset. Although they may benefit related recognition tasks, it is not easy to accurately detect such temporal segments. An automatic temporal segment detection framework using bilateral long short-term memory recurrent neural networks (BLSTM-RNN) to learn high-level temporal-spatial features, which synthesizes the local and global temporal-spatial information more efficiently, is presented. The framework is evaluated in detail over the face and body database (FABO). The comparison shows that the proposed framework outperforms state-of-the-art methods for solving the problem of temporal segment detection.

Virus Particle Detection by Convolutional Neural Network in Transmission Electron Microscopy Images.

Science.gov (United States)

Ito, Eisuke; Sato, Takaaki; Sano, Daisuke; Utagawa, Etsuko; Kato, Tsuyoshi

2018-06-01

A new computational method for the detection of virus particles in transmission electron microscopy (TEM) images is presented. Our approach is to use a convolutional neural network that transforms a TEM image to a probabilistic map that indicates where virus particles exist in the image. Our proposed approach automatically and simultaneously learns both discriminative features and classifier for virus particle detection by machine learning, in contrast to existing methods that are based on handcrafted features that yield many false positives and require several postprocessing steps. The detection performance of the proposed method was assessed against a dataset of TEM images containing feline calicivirus particles and compared with several existing detection methods, and the state-of-the-art performance of the developed method for detecting virus was demonstrated. Since our method is based on supervised learning that requires both the input images and their corresponding annotations, it is basically used for detection of already-known viruses. However, the method is highly flexible, and the convolutional networks can adapt themselves to any virus particles by learning automatically from an annotated dataset.

Male breast disease: clinical, mammographic, and ultrasonographic features

International Nuclear Information System (INIS)

Guenhan-Bilgen, Isil; Bozkaya, Halil; Uestuen, Esin Emin; Memis, Aysenur

2002-01-01

Purpose: To describe and quantitate the radiological (mammographic and ultrasonographic) characteristics of male breast disease and to report the clinical and pathological findings. Materials and methods: Two-hundred-thirty-six male patients with different male breast diseases, diagnosed at our institution between January 1990 and July 2001, were retrospectively evaluated. The history, physical examination, mammographic and ultrasonographic findings were analyzed. Results: The spectrum of the disease in 236 male patients were gynecomastia (n=206), primary breast carcinoma (n=14), fat necrosis (n=5), lipoma (n=3), subareolar abscess (n=2), epidermal inclusion cyst (n=1), sebaceous cyst (n=1), hematoma (n=1), myeloma (n=1), and metastatic carcinoma (n=2). The distribution of patterns of gynecomastia were; 34% (n=71) nodular, 35% (n=73) dendritic and 31% (n=62) diffuse glandular. Gynecomastia was unilateral in 55% (n=113) and bilateral in 45% (n=93) of the patients. Male breast cancer presented as a mass without microcalcifications in 86% (n=12) and with microcalcifications in 7% (n=1) of patients. The mass was obscured by gynecomastia, partially in two, totally in one patient. The location of the mass was retroareolar in 46% (n=6) and eccentric to the nipple in 54% (n=7) of patients. On ultrasonography (US), the contours were well-circumscribed in 20% (n=3) and irregular in 80% (n=12) of the masses. Conclusion: Male breast has a wide spectrum of diseases, some of which have characteristic radiological appearances that can be correlated with their pathologic diagnosis. In the evaluation of the male breast, mammography and US are essential and should be performed along with physical examination

Male breast disease: clinical, mammographic, and ultrasonographic features

Energy Technology Data Exchange (ETDEWEB)

Guenhan-Bilgen, Isil E-mail: isilbilgen@hotmail.com; Bozkaya, Halil; Uestuen, Esin Emin; Memis, Aysenur

2002-09-01

Purpose: To describe and quantitate the radiological (mammographic and ultrasonographic) characteristics of male breast disease and to report the clinical and pathological findings. Materials and methods: Two-hundred-thirty-six male patients with different male breast diseases, diagnosed at our institution between January 1990 and July 2001, were retrospectively evaluated. The history, physical examination, mammographic and ultrasonographic findings were analyzed. Results: The spectrum of the disease in 236 male patients were gynecomastia (n=206), primary breast carcinoma (n=14), fat necrosis (n=5), lipoma (n=3), subareolar abscess (n=2), epidermal inclusion cyst (n=1), sebaceous cyst (n=1), hematoma (n=1), myeloma (n=1), and metastatic carcinoma (n=2). The distribution of patterns of gynecomastia were; 34% (n=71) nodular, 35% (n=73) dendritic and 31% (n=62) diffuse glandular. Gynecomastia was unilateral in 55% (n=113) and bilateral in 45% (n=93) of the patients. Male breast cancer presented as a mass without microcalcifications in 86% (n=12) and with microcalcifications in 7% (n=1) of patients. The mass was obscured by gynecomastia, partially in two, totally in one patient. The location of the mass was retroareolar in 46% (n=6) and eccentric to the nipple in 54% (n=7) of patients. On ultrasonography (US), the contours were well-circumscribed in 20% (n=3) and irregular in 80% (n=12) of the masses. Conclusion: Male breast has a wide spectrum of diseases, some of which have characteristic radiological appearances that can be correlated with their pathologic diagnosis. In the evaluation of the male breast, mammography and US are essential and should be performed along with physical examination.

Ultrasonographic findings of uterine leiomyosarcoma: Differentiation from leiomyoma

International Nuclear Information System (INIS)

Song, Mi Jin; Kim, Jeong Ah

2003-01-01

To analyze the ultrasonographic findings of uterine leiomyosarcoma and to differentiate them from leiomyoma. From January 1998 to December 2001, a retrospective review of ultrasonographic findings of 7 patients with pathologically proven uterine leiomyosarcoma and 30 patients with leiomyoma was done. The mean size of leiomyosarcoma was 72 X 59 X 74 mm while the echogenicity, mixed. The shape of masses was round in five cases and ovoid in two. The margins of the mass were well-defined in five cases and partially indistinct in two. The locations of the mass were intramural in four cases, submucosal in two cases and indetermined in one case. The masses abutted the endometrium in four cases while invasion into the endometrium was seen in three. All masses were single. On the other hand, the mean size of leiomyoma was 52 X 45 X 49 mm. The echogenicities were homogenously hypoechoic in eighteen cases and mixed in twelve. The shape of masses was round in nineteen cases and ovoid in eleven cases. The margins of the mass were well-defined in twenty nine cases and partially indistinct in one case. The locations of the mass were intramural in twenty five cases, subserosal in four cases, and indetermined in one case. Separation between the masses and endometrium was definite in twenty two cases while the masses abutted the endometrium in seven cases. The invasion of the masses into the endometrium was noted in one case. Twenty five cases showed multiple masses while the remaining five cases were single mass. The possibility of leiomyosarcoma should be taken in to consideration when there is a single uterine mass larger than 7 cm that abutted the endometrium.

Ultrasonographic findings of uterine leiomyosarcoma: Differentiation from leiomyoma

Energy Technology Data Exchange (ETDEWEB)

Song, Mi Jin; Kim, Jeong Ah [Samsung Cheil Hospital, Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of)

2003-12-15

To analyze the ultrasonographic findings of uterine leiomyosarcoma and to differentiate them from leiomyoma. From January 1998 to December 2001, a retrospective review of ultrasonographic findings of 7 patients with pathologically proven uterine leiomyosarcoma and 30 patients with leiomyoma was done. The mean size of leiomyosarcoma was 72 X 59 X 74 mm while the echogenicity, mixed. The shape of masses was round in five cases and ovoid in two. The margins of the mass were well-defined in five cases and partially indistinct in two. The locations of the mass were intramural in four cases, submucosal in two cases and indetermined in one case. The masses abutted the endometrium in four cases while invasion into the endometrium was seen in three. All masses were single. On the other hand, the mean size of leiomyoma was 52 X 45 X 49 mm. The echogenicities were homogenously hypoechoic in eighteen cases and mixed in twelve. The shape of masses was round in nineteen cases and ovoid in eleven cases. The margins of the mass were well-defined in twenty nine cases and partially indistinct in one case. The locations of the mass were intramural in twenty five cases, subserosal in four cases, and indetermined in one case. Separation between the masses and endometrium was definite in twenty two cases while the masses abutted the endometrium in seven cases. The invasion of the masses into the endometrium was noted in one case. Twenty five cases showed multiple masses while the remaining five cases were single mass. The possibility of leiomyosarcoma should be taken in to consideration when there is a single uterine mass larger than 7 cm that abutted the endometrium.

A neural network detection system for lower-hybrid cavities in electron plasma density measured by the FREJA satellite

International Nuclear Information System (INIS)

Waldemark, J.; Karlsson, Jan

1995-03-01

This paper presents a lower-hybrid cavity detection system, CDS, for measurements of electron plasma density on the FREJA satellite wave experiment. The system can reduce the amount of data to be analysed by as much as 96% and still retain more than 85% of the desired information. The CDS is a combination of a hybrid neural network, HNN and expert rules. The HNN is a Self Organizing Map, SOM, combined with a feed forward back propagation neural net, BP. The CDS can be controlled by the user to operate with various degrees of sensitivity. Maximum detection capability is as high as 95% with data reduction lowered to 85%. 10 refs

Nanoparticle-based and bioengineered probes and sensors to detect physiological and pathological biomarkers in neural cells

Directory of Open Access Journals (Sweden)

Dusica eMaysinger

2015-12-01

Full Text Available Nanotechnology, a rapidly evolving field, provides simple and practical tools to investigate the nervous system in health and disease. Among these tools are nanoparticle-based probes and sensors that detect biochemical and physiological properties of neurons and glia, and generate signals proportionate to physical, chemical, and/or electrical changes in these cells. In this context, quantum dots (QDs, carbon-based structures (C-dots, graphene and nanodiamonds and gold nanoparticles are the most commonly used nanostructures. They can detect and measure enzymatic activities of proteases (metalloproteinases, caspases, ions, metabolites, and other biomolecules under physiological or pathological conditions in neural cells. Here, we provide some examples of nanoparticle-based and genetically engineered probes and sensors that are used to reveal changes in protease activities and calcium ion concentrations. Although significant progress in developing these tools has been made for probing neural cells, several challenges remain. We review many common hurdles in sensor development, while highlighting certain advances. In the end, we propose some future directions and ideas for developing practical tools for neural cell investigations, based on the maxim Measure what is measurable, and make measurable what is not so (Galileo Galilei.

Histopathologic Findings Related to the Indeterminate or Inadequate Results of Fine-Needle Aspiration Biopsy and Correlation with Ultrasonographic Findings in Papillary Thyroid Carcinomas

International Nuclear Information System (INIS)

Jung, So Lyung; Jung, Chan Kwon; Kim, Sung Hun; Kang, Bong Joo; Ahn, Kook Jin; Kim, Bum Soo; Ahn, Myeong Im; Im, Dong Jun; Bae, Ja Sung; Chung, Soo Kyo

2010-01-01

To determine histopathologic findings related to the indeterminate or inadequate result of fine-needle aspiration biopsy (FNAB) in papillary thyroid carcinomas (PTCs) and to correlate histopathological findings with ultrasonographic features of tumors. We retrospectively reviewed the medical records of FNAB, histopathologic characteristics, and sonographic findings of the solid portion of 95 PTCs in 95 patients. All cases were pathologically confirmed by surgery. Histopathologic characteristics were analyzed for tumor distribution, microcystic changes, fibrosis, and tumor component. We assumed several histopathologic conditions to be the cause of indeterminate or inadequate results of FNAB, including: 1) an uneven tumor distribution, 2) > 30% microcystic changes, 3) > 30% fibrosis, and 4) < 30% tumor component. Ultrasonographic findings of each PTC were evaluated for echotexture (homogeneous or heterogeneous), echogenicity (markedly hypoechoic, hypoechoic, isoechoic, or hyperechoic), and volume of the nodule. We correlated histopathologic characteristics of the PTC with results of the FNAB and ultrasonographic findings. From 95 FNABs, 71 cases (74%) were confirmed with malignancy or suspicious malignancy (PTCs), 21 (22%) had indeterminate results (atypical cells), and three (4%) were negative for malignancy. None of the assumed variables influenced the diagnostic accuracy of FNAB. Tumor distribution and fibrosis were statistically correlated with ultrasonographic findings of the PTCs (p < 0.05). Uneven tumor distribution was related with small tumor volume, and fibrosis over 30% was correlated with homogeneous echotexture, markedly hypoechoic and hypoechoic echogenicity, and small tumor volume (p < 0.05). No histopathologic component was found to correlate with improper results of FNAB in PTCs. In contrast, two histopathologic characteristics, uneven distribution and fibrosis, were correlated with ultrasonographic findings

No effects of PRP on ultrasonographic tendon structure and neovascularisation in chronic midportion Achilles tendinopathy

NARCIS (Netherlands)

de Vos, R. J.; Weir, A.; Tol, J. L.; Verhaar, J. A. N.; Weinans, H.; van Schie, H. T. M.

2011-01-01

To assess whether a platelet-rich plasma (PRP) injection leads to an enhanced tendon structure and neovascularisation, measured with ultrasonographic techniques, in chronic midportion Achilles tendinopathy. Double-blind, randomised, placebo-controlled clinical trial. Sports medical department of The

Ultrasonographic findings of psoas abscess and hematoma

International Nuclear Information System (INIS)

Kim, Eun Kyung; Lim, Jae Hoon; Ko, Young Tae; Choi, Yong Dae; Kim, Ho Kyun; Kim, Soon Yong

1984-01-01

A retrospective analysis of the ultrasonographic findings of 9 cases tuberculous abscess, 5 cases of pyogenic abscess and 2 cases of hematoma of psoas and adjacent muscles was made. Fluid collection with or without internal echoes was seen in 12 cases out of total 16 cases. Other findings were 2 cases of only muscle swelling, 1 cases of highly echogenic mass-like appearance and 1 case of fluid collection with septae. Ultrasonography is considered an accurate method in identifying early pathologic changes of the psoas muscle and determining its extent, and in differentiating tumor from fluid collection of the psoas muscle. Authors dare to say that ultrasound examination is a procedure of choice in the diagnosis of psoas abscess and hematoma

Neural network pattern recognition of lingual-palatal pressure for automated detection of swallow.

Science.gov (United States)

Hadley, Aaron J; Krival, Kate R; Ridgel, Angela L; Hahn, Elizabeth C; Tyler, Dustin J

2015-04-01

We describe a novel device and method for real-time measurement of lingual-palatal pressure and automatic identification of the oral transfer phase of deglutition. Clinical measurement of the oral transport phase of swallowing is a complicated process requiring either placement of obstructive sensors or sitting within a fluoroscope or articulograph for recording. Existing detection algorithms distinguish oral events with EMG, sound, and pressure signals from the head and neck, but are imprecise and frequently result in false detection. We placed seven pressure sensors on a molded mouthpiece fitting over the upper teeth and hard palate and recorded pressure during a variety of swallow and non-swallow activities. Pressure measures and swallow times from 12 healthy and 7 Parkinson's subjects provided training data for a time-delay artificial neural network to categorize the recordings as swallow or non-swallow events. User-specific neural networks properly categorized 96 % of swallow and non-swallow events, while a generalized population-trained network was able to properly categorize 93 % of swallow and non-swallow events across all recordings. Lingual-palatal pressure signals are sufficient to selectively and specifically recognize the initiation of swallowing in healthy and dysphagic patients.

Automated detection of lung nodules with three-dimensional convolutional neural networks

Science.gov (United States)

Pérez, Gustavo; Arbeláez, Pablo

2017-11-01

Lung cancer is the cancer type with highest mortality rate worldwide. It has been shown that early detection with computer tomography (CT) scans can reduce deaths caused by this disease. Manual detection of cancer nodules is costly and time-consuming. We present a general framework for the detection of nodules in lung CT images. Our method consists of the pre-processing of a patient's CT with filtering and lung extraction from the entire volume using a previously calculated mask for each patient. From the extracted lungs, we perform a candidate generation stage using morphological operations, followed by the training of a three-dimensional convolutional neural network for feature representation and classification of extracted candidates for false positive reduction. We perform experiments on the publicly available LIDC-IDRI dataset. Our candidate extraction approach is effective to produce precise candidates with a recall of 99.6%. In addition, false positive reduction stage manages to successfully classify candidates and increases precision by a factor of 7.000.

Convolutional Neural Network-Based Shadow Detection in Images Using Visible Light Camera Sensor

Directory of Open Access Journals (Sweden)

Dong Seop Kim

2018-03-01

Full Text Available Recent developments in intelligence surveillance camera systems have enabled more research on the detection, tracking, and recognition of humans. Such systems typically use visible light cameras and images, in which shadows make it difficult to detect and recognize the exact human area. Near-infrared (NIR light cameras and thermal cameras are used to mitigate this problem. However, such instruments require a separate NIR illuminator, or are prohibitively expensive. Existing research on shadow detection in images captured by visible light cameras have utilized object and shadow color features for detection. Unfortunately, various environmental factors such as illumination change and brightness of background cause detection to be a difficult task. To overcome this problem, we propose a convolutional neural network-based shadow detection method. Experimental results with a database built from various outdoor surveillance camera environments, and from the context-aware vision using image-based active recognition (CAVIAR open database, show that our method outperforms previous works.

Cellular neural networks for motion estimation and obstacle detection

Directory of Open Access Journals (Sweden)

D. Feiden

2003-01-01

Full Text Available Obstacle detection is an important part of Video Processing because it is indispensable for a collision prevention of autonomously navigating moving objects. For example, vehicles driving without human guidance need a robust prediction of potential obstacles, like other vehicles or pedestrians. Most of the common approaches of obstacle detection so far use analytical and statistical methods like motion estimation or generation of maps. In the first part of this contribution a statistical algorithm for obstacle detection in monocular video sequences is presented. The proposed procedure is based on a motion estimation and a planar world model which is appropriate to traffic scenes. The different processing steps of the statistical procedure are a feature extraction, a subsequent displacement vector estimation and a robust estimation of the motion parameters. Since the proposed procedure is composed of several processing steps, the error propagation of the successive steps often leads to inaccurate results. In the second part of this contribution it is demonstrated, that the above mentioned problems can be efficiently overcome by using Cellular Neural Networks (CNN. It will be shown, that a direct obstacle detection algorithm can be easily performed, based only on CNN processing of the input images. Beside the enormous computing power of programmable CNN based devices, the proposed method is also very robust in comparison to the statistical method, because is shows much less sensibility to noisy inputs. Using the proposed approach of obstacle detection in planar worlds, a real time processing of large input images has been made possible.

Comparison of pixel -based and artificial neural networks classification methods for detecting forest cover changes in Malaysia

International Nuclear Information System (INIS)

Deilmai, B R; Rasib, A W; Ariffin, A; Kanniah, K D

2014-01-01

According to the FAO (Food and Agriculture Organization), Malaysia lost 8.6% of its forest cover between 1990 and 2005. In forest cover change detection, remote sensing plays an important role. A lot of change detection methods have been developed, and most of them are semi-automated. These methods are time consuming and difficult to apply. One of the new and robust methods for change detection is artificial neural network (ANN). In this study, (ANN) classification scheme is used to detect the forest cover changes in the Johor state in Malaysia. Landsat Thematic Mapper images covering a period of 9 years (2000 and 2009) are used. Results obtained with ANN technique was compared with Maximum likelihood classification (MLC) to investigate whether ANN can perform better in the tropical environment. Overall accuracy of the ANN and MLC techniques are 75%, 68% (2000) and 80%, 75% (2009) respectively. Using the ANN method, it was found that forest area in Johor decreased as much as 1298 km2 between 2000 and 2009. The results also showed the potential and advantages of neural network in classification and change detection analysis

Feature Extraction and Fusion Using Deep Convolutional Neural Networks for Face Detection

Directory of Open Access Journals (Sweden)

Xiaojun Lu

2017-01-01

Full Text Available This paper proposes a method that uses feature fusion to represent images better for face detection after feature extraction by deep convolutional neural network (DCNN. First, with Clarifai net and VGG Net-D (16 layers, we learn features from data, respectively; then we fuse features extracted from the two nets. To obtain more compact feature representation and mitigate computation complexity, we reduce the dimension of the fused features by PCA. Finally, we conduct face classification by SVM classifier for binary classification. In particular, we exploit offset max-pooling to extract features with sliding window densely, which leads to better matches of faces and detection windows; thus the detection result is more accurate. Experimental results show that our method can detect faces with severe occlusion and large variations in pose and scale. In particular, our method achieves 89.24% recall rate on FDDB and 97.19% average precision on AFW.

Detection of normal plantar fascia thickness in adults via the ultrasonographic method.

Science.gov (United States)

Abul, Kadir; Ozer, Devrim; Sakizlioglu, Secil Sezgin; Buyuk, Abdul Fettah; Kaygusuz, Mehmet Akif

2015-01-01

Heel pain is a prevalent concern in orthopedic clinics, and there are numerous pathologic abnormalities that can cause heel pain. Plantar fasciitis is the most common cause of heel pain, and the plantar fascia thickens in this process. It has been found that thickening to greater than 4 mm in ultrasonographic measurements can be accepted as meaningful in diagnoses. Herein, we aimed to measure normal plantar fascia thickness in adults using ultrasonography. We used ultrasonography to measure the plantar fascia thickness of 156 healthy adults in both feet between April 1, 2011, and June 30, 2011. These adults had no previous heel pain. The 156 participants comprised 88 women (56.4%) and 68 men (43.6%) (mean age, 37.9 years; range, 18-65 years). The weight, height, and body mass index of the participants were recorded, and statistical analyses were conducted. The mean ± SD (range) plantar fascia thickness measurements for subgroups of the sample were as follows: 3.284 ± 0.56 mm (2.4-5.1 mm) for male right feet, 3.3 ± 0.55 mm (2.5-5.0 mm) for male left feet, 2.842 ± 0.42 mm (1.8-4.1 mm) for female right feet, and 2.8 ± 0.44 mm (1.8-4.3 mm) for female left feet. The overall mean ± SD (range) thickness for the right foot was 3.035 ± 0.53 mm (1.8-5.1 mm) and for the left foot was 3.053 ± 0.54 mm (1.8-5.0 mm). There was a statistically significant and positive correlation between plantar fascia thickness and participant age, weight, height, and body mass index. The plantar fascia thickness of adults without heel pain was measured to be less than 4 mm in most participants (~92%). There was no statistically significant difference between the thickness of the right and left foot plantar fascia.

Spiral Computed Tomographic Imaging Related to Computerized Ultrasonographic Images of Carotid Plaque Morphology and Histology

DEFF Research Database (Denmark)

Grønholdt, Marie-Louise M.; Wagner, Aase; Wiebe, Britt M.

2001-01-01

Echolucency of carotid atherosclerotic plaques, as evaluated by computerized B-mode ultrasonographic images, has been associated with an increased incidence of brain infarcts on cerebral computed tomographic scans. We tested the hypotheses that characterization of carotid plaques on spiral comput...

Convolutional neural network features based change detection in satellite images

Science.gov (United States)

Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

2016-07-01

With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

Accurate Natural Trail Detection Using a Combination of a Deep Neural Network and Dynamic Programming.

Science.gov (United States)

Adhikari, Shyam Prasad; Yang, Changju; Slot, Krzysztof; Kim, Hyongsuk

2018-01-10

This paper presents a vision sensor-based solution to the challenging problem of detecting and following trails in highly unstructured natural environments like forests, rural areas and mountains, using a combination of a deep neural network and dynamic programming. The deep neural network (DNN) concept has recently emerged as a very effective tool for processing vision sensor signals. A patch-based DNN is trained with supervised data to classify fixed-size image patches into "trail" and "non-trail" categories, and reshaped to a fully convolutional architecture to produce trail segmentation map for arbitrary-sized input images. As trail and non-trail patches do not exhibit clearly defined shapes or forms, the patch-based classifier is prone to misclassification, and produces sub-optimal trail segmentation maps. Dynamic programming is introduced to find an optimal trail on the sub-optimal DNN output map. Experimental results showing accurate trail detection for real-world trail datasets captured with a head mounted vision system are presented.

Automated embolic signal detection using Deep Convolutional Neural Network.

Science.gov (United States)

Sombune, Praotasna; Phienphanich, Phongphan; Phuechpanpaisal, Sutanya; Muengtaweepongsa, Sombat; Ruamthanthong, Anuchit; Tantibundhit, Charturong

2017-07-01

This work investigated the potential of Deep Neural Network in detection of cerebral embolic signal (ES) from transcranial Doppler ultrasound (TCD). The resulting system is aimed to couple with TCD devices in diagnosing a risk of stroke in real-time with high accuracy. The Adaptive Gain Control (AGC) approach developed in our previous study is employed to capture suspected ESs in real-time. By using spectrograms of the same TCD signal dataset as that of our previous work as inputs and the same experimental setup, Deep Convolutional Neural Network (CNN), which can learn features while training, was investigated for its ability to bypass the traditional handcrafted feature extraction and selection process. Extracted feature vectors from the suspected ESs are later determined whether they are of an ES, artifact (AF) or normal (NR) interval. The effectiveness of the developed system was evaluated over 19 subjects going under procedures generating emboli. The CNN-based system could achieve in average of 83.0% sensitivity, 80.1% specificity, and 81.4% accuracy, with considerably much less time consumption in development. The certainly growing set of training samples and computational resources will contribute to high performance. Besides having potential use in various clinical ES monitoring settings, continuation of this promising study will benefit developments of wearable applications by leveraging learnable features to serve demographic differentials.

Detection of bars in galaxies using a deep convolutional neural network

Science.gov (United States)

Abraham, Sheelu; Aniyan, A. K.; Kembhavi, Ajit K.; Philip, N. S.; Vaghmare, Kaustubh

2018-06-01

We present an automated method for the detection of bar structure in optical images of galaxies using a deep convolutional neural network that is easy to use and provides good accuracy. In our study, we use a sample of 9346 galaxies in the redshift range of 0.009-0.2 from the Sloan Digital Sky Survey (SDSS), which has 3864 barred galaxies, the rest being unbarred. We reach a top precision of 94 per cent in identifying bars in galaxies using the trained network. This accuracy matches the accuracy reached by human experts on the same data without additional information about the images. Since deep convolutional neural networks can be scaled to handle large volumes of data, the method is expected to have great relevance in an era where astronomy data is rapidly increasing in terms of volume, variety, volatility, and velocity along with other V's that characterize big data. With the trained model, we have constructed a catalogue of barred galaxies from SDSS and made it available online.

Ultrasonographic identification of the cricothyroid membrane

DEFF Research Database (Denmark)

Kristensen, M S; Teoh, W H; Rudolph, S S

2016-01-01

Inability to identify the cricothyroid membrane by inspection and palpation contributes substantially to the high failure rate of cricothyrotomy. This narrative review summarizes the current evidence for application of airway ultrasonography for identification of the cricothyroid membrane compare...... ultrasonographic identification; a service that we should aim at making available in all locations where anaesthesia is undertaken and where patients with difficult airways could be encountered.......Inability to identify the cricothyroid membrane by inspection and palpation contributes substantially to the high failure rate of cricothyrotomy. This narrative review summarizes the current evidence for application of airway ultrasonography for identification of the cricothyroid membrane compared...... with the clinical techniques. We identified the best-documented techniques for bedside use, their success rates, and the necessary training for airway-ultrasound-naïve clinicians. After a short but structured training, the cricothyroid membrane can be identified using ultrasound in difficult patients by previously...

Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing

Directory of Open Access Journals (Sweden)

R. B. Santos

2014-03-01

Full Text Available Considering the importance of monitoring pipeline systems, this work presents the development of a technique to detect gas leakage in pipelines, based on an acoustic method, and on-line prediction of leak magnitude using artificial neural networks. On-line audible noises generated by leakage were obtained with a microphone installed in a 60 m long pipeline. The sound noises were decomposed into sounds of different frequencies: 1 kHz, 5 kHz and 9 kHz. The dynamics of these noises in time were used as input to the neural model in order to determine the occurrence and the leak magnitude. The results indicated the great potential of the technique and of the developed neural network models. For all on-line tests, the models showed 100% accuracy in leak detection, except for a small orifice (1 mm under 4 kgf/cm² of nominal pressure. Similarly, the neural network models could adequately predict the magnitude of the leakages.

Low-temperature infiltration identified using infrared thermography in patients with subcutaneous edema revealed ultrasonographically: A case report.

Science.gov (United States)

Oya, Maiko; Takahashi, Toshiaki; Tanabe, Hidenori; Oe, Makoto; Murayama, Ryoko; Yabunaka, Koichi; Matsui, Yuko; Sanada, Hiromi

Infiltration is a frequent complication of infusion therapy. We previously demonstrated the usefulness of infrared thermography as an objective method of detecting infiltration in healthy people. However, whether thermography can detect infiltration in clinical settings remains unknown. Therefore, we report two cases where thermography was useful in detecting infiltration at puncture sites. In both cases, tissue changes were verified ultrasonographically. The patients were a 56-year-old male with cholangitis and a 76-year-old female with hepatoma. In both cases, infiltration symptoms such as swelling and erythema occurred one day after the insertion of a peripheral intravenous catheter. Thermographic images from both patients revealed low-temperature areas spreading from the puncture sites; however, these changes were not observed in other patients. The temperature difference between the low-temperature areas and their surrounding skin surface exceeded 1.0°C. Concurrently, ultrasound images revealed that tissues surrounding the vein had a cobblestone appearance, indicating edema. In both patients, subcutaneous tissue changes suggested infiltration and both had low-temperature areas spreading from the puncture sites. Thus, subcutaneous edema may indicate infusion leakage, resulting in a decrease in the temperature of the associated skin surface. These cases suggest that infrared thermography is an effective method of objectively and noninvasively detecting infiltration.

Spontaneous infarction of benign breast lesion during pregnancy: Ultrasonographic and pathologic findings

Energy Technology Data Exchange (ETDEWEB)

Kim, Jin Young; Kim, Kyu Soon; Kim, Ju Hun [Eulji University Hospital, Daejeon (Korea, Republic of); Lee, Yun Hak [Dept. of Radiology, Health Care Center, Pohang (Korea, Republic of)

2015-10-15

The spontaneous infarction of benign breast lesions is a rare entity and hence is not usually considered in the differential diagnosis during radiologic or clinical examination. There have been a few published cases of infarction during pregnancy and lactation. In this study we report the ultrasonographic and pathologic features of a spontaneous infarction of a lactating adenoma with acute mastitis and abscess and a spontaneously infarcted fibroadenoma.

Spontaneous infarction of benign breast lesion during pregnancy: Ultrasonographic and pathologic findings

International Nuclear Information System (INIS)

Kim, Jin Young; Kim, Kyu Soon; Kim, Ju Hun; Lee, Yun Hak

2015-01-01

The spontaneous infarction of benign breast lesions is a rare entity and hence is not usually considered in the differential diagnosis during radiologic or clinical examination. There have been a few published cases of infarction during pregnancy and lactation. In this study we report the ultrasonographic and pathologic features of a spontaneous infarction of a lactating adenoma with acute mastitis and abscess and a spontaneously infarcted fibroadenoma

Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network.

Science.gov (United States)

Diniz, Pedro Henrique Bandeira; Valente, Thales Levi Azevedo; Diniz, João Otávio Bandeira; Silva, Aristófanes Corrêa; Gattass, Marcelo; Ventura, Nina; Muniz, Bernardo Carvalho; Gasparetto, Emerson Leandro

2018-04-19

White matter lesions are non-static brain lesions that have a prevalence rate up to 98% in the elderly population. Because they may be associated with several brain diseases, it is important that they are detected as soon as possible. Magnetic Resonance Imaging (MRI) provides three-dimensional data with the possibility to detect and emphasize contrast differences in soft tissues, providing rich information about the human soft tissue anatomy. However, the amount of data provided for these images is far too much for manual analysis/interpretation, representing a difficult and time-consuming task for specialists. This work presents a computational methodology capable of detecting regions of white matter lesions of the brain in MRI of FLAIR modality. The techniques highlighted in this methodology are SLIC0 clustering for candidate segmentation and convolutional neural networks for candidate classification. The methodology proposed here consists of four steps: (1) images acquisition, (2) images preprocessing, (3) candidates segmentation and (4) candidates classification. The methodology was applied on 91 magnetic resonance images provided by DASA, and achieved an accuracy of 98.73%, specificity of 98.77% and sensitivity of 78.79% with 0.005 of false positives, without any false positives reduction technique, in detection of white matter lesion regions. It is demonstrated the feasibility of the analysis of brain MRI using SLIC0 and convolutional neural network techniques to achieve success in detection of white matter lesions regions. Copyright © 2018. Published by Elsevier B.V.

Ultrasonographic assessment of skin structure according to age

Directory of Open Access Journals (Sweden)

Diana Crisan

2012-01-01

Full Text Available Background: High-frequency ultrasound is a noninvasive tool that offers characteristic markers, quantifying the cutaneous changes of the physiological senescence process. Aims: The aim was to assess the changes in skin thickness, dermal density and echogenicity, as part of the ageing process, with different age intervals. Methods : The study was performed on 160 patients, aged 40.4 ± 21.2, divided into four age categories: <20, 21-40, 41-60, 61-80. Ultrasonographic images (Dermascan device were taken from three sites: dorsal forearm (DF, medial arm (MA, zygomatic area (ZA. We assessed the thickness of epidermis and dermis (mm, number of low, medium, high echogenicity pixels, the ratio between the echogenicity of the upper and lower dermis (LEPs/LEPi, and SLEB (subepidermal low echogenicity band. The statistical analysis was performed using SPSS 15.00. A P value <0.05 was considered significant. Results: On all examined sites, it was found that the dermal thickness increases in the 21 to 40 year interval (P<0.0001. After the 21 to 40 year interval, the number of low echogenic pixels increases significantly, especially on photoexposed sites. High-echogenic pixels follow the same pattern on all examined sites: they increase in the 21 to 40 year interval and decrease in the 3rd and 4th age category. The LEPs/LEPi ratio increases significantly with age, at all sites (P<0.05, due to an increase of hypoechogenic pixels in the upper dermis. Conclusions: High-frequency ultrasound is a noninvasive "histological" tool that can assess the cutaneous structure and age-related changes. It offers imagistic markers, comparable to the histological parameters and also characteristic ultrasonographic markers. Histology remains the gold standard for the investigation of the integumentary system.

Vision-Based Fall Detection with Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

Adrián Núñez-Marcos

2017-01-01

Full Text Available One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities. Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe that the irruption of the Smart Environments and the Internet of Things paradigms, together with the increasing number of cameras in our daily environment, forms an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a sequence of frames contains a person falling. To model the video motion and make the system scenario independent, we use optical flow images as input to the networks followed by a novel three-step training phase. Furthermore, our method is evaluated in three public datasets achieving the state-of-the-art results in all three of them.

Blunt trauma to the spleen: ultrasonographic findings

Energy Technology Data Exchange (ETDEWEB)

Doody, O. [Department of Radiology, Tallaght Hospital, Dublin (Ireland); Lyburn, D. [Department of Radiology, Cheltenham General Hospital (United Kingdom); Geoghegan, T. [Department of Radiology, Tallaght Hospital, Dublin (Ireland); Govender, P. [Department of Radiology, Tallaght Hospital, Dublin (Ireland); Monk, P.M. [Department of Radiology, Vancouver Hospital (Canada); Torreggiani, W.C. [Department of Radiology, Tallaght Hospital, Dublin (Ireland)]. E-mail: william.torreggiani@amnch.ie

2005-09-01

The spleen is the most frequently injured organ in adults who sustain blunt abdominal trauma. Splenic trauma accounts for approximately 25% to 30% of all intra-abdominal injuries. The management of splenic injury has undergone rapid change over the last decade, with increasing emphasis on splenic salvage and non-operative management. Identifying the presence and degree of splenic injury is critical in triaging the management of patients. Imaging is integral in the identification of splenic injuries, both at the time of injury and during follow-up. Although CT remains the gold standard in blunt abdominal trauma, US continues to play an important role in assessing the traumatized spleen. This pictorial review illustrates the various ultrasonographic appearances of the traumatized spleen. Correlation with other imaging is presented and complications that occur during follow-up are described.

Ultrasonographic study of deep portion of diencephalo-telencephalic vesicle for the determination of gestational age of the canine foetus.

Science.gov (United States)

Beccaglia, M; Faustini, M; Luvoni, G C

2008-06-01

The aim of this study was to evaluate the accuracy of ultrasonographic measurement of the deep portion of diencephalo-telencephalic vesicle (DPTV) in the prediction of delivery date in different size bitches. The effects of litter size and foetal sex ratio on the accuracy were also investigated. For this purpose, the growth curve of DPTV was derived in large size dogs (26-40 kg) and the results of the accuracy of the prediction were compared with those obtained in small (dogs by the application of the equations derived from the growth curve previously described. Ultrasonographic examinations were performed once a week during the second half of pregnancy in seven large size bitches (26-40 kg body weight). A linear regression model was adopted to analyse the relationship between the DPTV mean values and the days remaining to parturition. The results of regression analysis indicated that DPTV measurement in large size dogs is significantly and linearly related to the gestational age. Ultrasonographic measurements of DPTV were also performed during pregnancy in different size bitches with unknown breeding dates. Although the results indicated a similar accuracy of the prediction of the date of parturition in the different sizes of bitches, a higher accuracy was obtained in normal and large litter size compared with small litters. Foetal sex ratio did not affect the accuracy. In conclusion, this study demonstrated that the accuracy of the prediction of parturition day obtained by ultrasonographic evaluation of DPTV growth is reliable when specific formulae for different size dogs are applied. It should be noted that although foetal sex ratio does not affect the accuracy, the prediction could be less accurate when a small litter is present.

Artificial neural network aided non-invasive grading evaluation of hepatic fibrosis by duplex ultrasonography

Directory of Open Access Journals (Sweden)

Zhang Li

2012-06-01

Full Text Available Abstract Background Artificial neural networks (ANNs are widely studied for evaluating diseases. This paper discusses the intelligence mode of an ANN in grading the diagnosis of liver fibrosis by duplex ultrasonogaphy. Methods 239 patients who were confirmed as having liver fibrosis or cirrhosis by ultrasound guided liver biopsy were investigated in this study. We quantified ultrasonographic parameters as significant parameters using a data optimization procedure applied to an ANN. 179 patients were typed at random as the training group; 60 additional patients were consequently enrolled as the validating group. Performance of the ANN was evaluated according to accuracy, sensitivity, specificity, Youden’s index and receiver operating characteristic (ROC analysis. Results 5 ultrasonographic parameters; i.e., the liver parenchyma, thickness of spleen, hepatic vein (HV waveform, hepatic artery pulsatile index (HAPI and HV damping index (HVDI, were enrolled as the input neurons in the ANN model. The sensitivity, specificity and accuracy of the ANN model for quantitative diagnosis of liver fibrosis were 95.0%, 85.0% and 88.3%, respectively. The Youden’s index (YI was 0.80. Conclusions The established ANN model had good sensitivity and specificity in quantitative diagnosis of hepatic fibrosis or liver cirrhosis. Our study suggests that the ANN model based on duplex ultrasound may help non-invasive grading diagnosis of liver fibrosis in clinical practice.

Early detection of incipient faults in power plants using accelerated neural network learning

International Nuclear Information System (INIS)

Parlos, A.G.; Jayakumar, M.; Atiya, A.

1992-01-01

An important aspect of power plant automation is the development of computer systems able to detect and isolate incipient (slowly developing) faults at the earliest possible stages of their occurrence. In this paper, the development and testing of such a fault detection scheme is presented based on recognition of sensor signatures during various failure modes. An accelerated learning algorithm, namely adaptive backpropagation (ABP), has been developed that allows the training of a multilayer perceptron (MLP) network to a high degree of accuracy, with an order of magnitude improvement in convergence speed. An artificial neural network (ANN) has been successfully trained using the ABP algorithm, and it has been extensively tested with simulated data to detect and classify incipient faults of various types and severity and in the presence of varying sensor noise levels

Detection of high-grade small bowel obstruction on conventional radiography with convolutional neural networks.

Science.gov (United States)

Cheng, Phillip M; Tejura, Tapas K; Tran, Khoa N; Whang, Gilbert

2018-05-01

The purpose of this pilot study is to determine whether a deep convolutional neural network can be trained with limited image data to detect high-grade small bowel obstruction patterns on supine abdominal radiographs. Grayscale images from 3663 clinical supine abdominal radiographs were categorized into obstructive and non-obstructive categories independently by three abdominal radiologists, and the majority classification was used as ground truth; 74 images were found to be consistent with small bowel obstruction. Images were rescaled and randomized, with 2210 images constituting the training set (39 with small bowel obstruction) and 1453 images constituting the test set (35 with small bowel obstruction). Weight parameters for the final classification layer of the Inception v3 convolutional neural network, previously trained on the 2014 Large Scale Visual Recognition Challenge dataset, were retrained on the training set. After training, the neural network achieved an AUC of 0.84 on the test set (95% CI 0.78-0.89). At the maximum Youden index (sensitivity + specificity-1), the sensitivity of the system for small bowel obstruction is 83.8%, with a specificity of 68.1%. The results demonstrate that transfer learning with convolutional neural networks, even with limited training data, may be used to train a detector for high-grade small bowel obstruction gas patterns on supine radiographs.

Ultrasonographic examination of the forestomachs and the abomasum in ruminal drinker calves

Science.gov (United States)

2013-01-01

Background The study investigated the ultrasonographic appearance of the reticulum, rumen, omasum and abomasum of calves with ruminal drinking syndrome. Methods In ten milk-fed calves with ruminal drinking syndrome the reticulum, rumen, omasum and abomasum were examined by ultrasonography using a 5-MHz linear transducer before, during and after the ingestion of milk. Results The reticulum could be imaged in eight of ten calves before feeding. The reticular wall appeared as an echoic line, similar to mature cattle, and reticular folds were seen in eight calves. The reticular content appeared as echoic heterogeneous fluid. Reticular contractions were biphasic with 1.0 ± 0.38 contractions per minute. The rumen had a mean wall thickness of 2.1 mm dorsally, 3.5 mm at the level of the longitudinal groove, and 3.2 mm ventrally. The ventral sac of the rumen of all calves contained echoic heterogeneous liquid. During feeding the milk entering the rumen could be seen as hyperechoic liquid in five calves. The omasum was seen on the right side as a crescent-shaped line medial to the liver in seven calves. Only the omasal wall closest to the transducer was seen as an echoic line with a mean thickness of 2.7 mm. The ultrasonographic appearance of the omasum did not change during or after feeding. The abomasum was seen immediately caudal to the xyphoid on both sides of the midline before feeding. The mean length at the ventral midline was 22.2 cm. The ingesta were heterogeneous in all calves and the abomasal folds were distinct in eight. The mean lateral expansion of the abomasum from the ventral midline to the left and right varied from 8.7 to 13.8 cm and from 4.3 to 11.3 cm. The milk entering the abomasum was observed in all calves, and signs of milk clotting were seen in all calves 15 minutes after feeding. Conclusion This study showed that ultrasonography is useful for detecting milk in the reticulum and rumen of calves with ruminal drinking syndrome. PMID:23298472

Detecting and Predicting Muscle Fatigue during Typing By SEMG Signal Processing and Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Elham Ghoochani

2011-03-01

Full Text Available Introduction: Repetitive strain injuries are one of the most prevalent problems in occupational diseases. Repetition, vibration and bad postures of the extremities are physical risk factors related to work that can cause chronic musculoskeletal disorders. Repetitive work on a computer with low level contraction requires the posture to be maintained for a long time, which can cause muscle fatigue. Muscle fatigue in shoulders and neck is one of the most prevalent problems reported with computer users especially during typing. Surface electromyography (SEMG signals are used for detecting muscle fatigue as a non-invasive method. Material and Methods: Nine healthy females volunteered for signal recoding during typing. EMG signals were recorded from the trapezius muscle, which is subjected to muscle fatigue during typing.  After signal analysis and feature extraction, detecting and predicting muscle fatigue was performed by using the MLP artificial neural network. Results: Recorded signals were analyzed in time and frequency domains for feature extraction. Results of classification showed that the MLP neural network can detect and predict muscle fatigue during typing with 80.79 % ± 1.04% accuracy. Conclusion: Intelligent classification and prediction of muscle fatigue can have many applications in human factors engineering (ergonomics, rehabilitation engineering and biofeedback equipment for mitigating the injuries of repetitive works.

Fault detection and diagnosis in asymmetric multilevel inverter using artificial neural network

Science.gov (United States)

Raj, Nithin; Jagadanand, G.; George, Saly

2018-04-01

The increased component requirement to realise multilevel inverter (MLI) fallout in a higher fault prospect due to power semiconductors. In this scenario, efficient fault detection and diagnosis (FDD) strategies to detect and locate the power semiconductor faults have to be incorporated in addition to the conventional protection systems. Even though a number of FDD methods have been introduced in the symmetrical cascaded H-bridge (CHB) MLIs, very few methods address the FDD in asymmetric CHB-MLIs. In this paper, the gate-open circuit FDD strategy in asymmetric CHB-MLI is presented. Here, a single artificial neural network (ANN) is used to detect and diagnose the fault in both binary and trinary configurations of the asymmetric CHB-MLIs. In this method, features of the output voltage of the MLIs are used as to train the ANN for FDD method. The results prove the validity of the proposed method in detecting and locating the fault in both asymmetric MLI configurations. Finally, the ANN response to the input parameter variation is also analysed to access the performance of the proposed ANN-based FDD strategy.

DIGITAL DETECTION SYSTEM DESIGN OF MYCOBACTERIUM TUBERCULOSIS THROUGH EXTRACTION OF SPUTUM IMAGE USING NEURAL NETWORK METHOD

Directory of Open Access Journals (Sweden)

Franky Arisgraha

2012-01-01

Full Text Available Tuberculosis (TBC is an dangerous disease and many people has been infected. One of many important steps to control TBC effectively and efficiently is by increasing case finding using right method and accurate diagnostic. One of them is to detect Mycobacterium Tuberculosis inside sputum. Conventional detection of Mycobacterium Tuberculosis inside sputum can need a lot of time, so digitally detection method of Mycobacterium Tuberculosis was designed as an effort to get better result of detection. This method was designed by using combination between digital image processing method and Neural Network method. From testing report that was done, Mycobacterium can be detected with successful value reach 77.5% and training error less than 5%.

Radiographic and ultrasonographic features of hypertrophic feline muscular dystrophy in two cats

International Nuclear Information System (INIS)

Berry, C.R.; Gaschen, F.P.; Ackerman, N.

1992-01-01

Hypertrophic fellne musculer dystrophy has been reported as an X-linked inherited deficiency of a cytoskeletal myofiber protein called dystrophin. This report deserlbes the radiographic and ultrasonographic abnormalities of two male littermate domestic short-hair cats and reviews the previous reported findings assoclated with hypertrophic feline muscular dystrophy. The thoracic radiographic abnormalities included: progressive cardiomegaly, large convex, scalloped irregularities associated with the vetral aspect of the diaphragm, and variable degrees of esophageal dilation (megaesophagus) with associated cranioventral aspiration pneumonia. Echocardiographic features included: concentric left vetricular wall thickening, increased left ventricular and diastolic and systolic dimensions, and an increase in endocardial echogenicity. Abdominal radiographic abnormalities included: hepatosplenomegaly, peritoneal effusion, renomegaly, adrenal gland mineralization, and paralumbar and diaphragmatic musculature enlargement. Abdomlnal ultrasonographic abnormalities included: irregularly thickened muscular portion of the diaphragm; hypoechogenicity of the liver; peritoneal effusion; hepatosplenomegaly; renomegaly with hyperechoic cortex and medulla; and adrenal gland mineralization. The irregular scalloped appearance of the diaphragm (particularly along the ventral/sternal margin) was a consistenl radiographic abnormlity in the two cats with hypertrophic feline muscular dystrophy after the age of 7 months. This finding was confirmed by ultrasound as a thickened irregular, hyperechoic diaphragm. A diagnosis of hypertrophic feline muscular dystrophy should be strongly suspected if this abnormality is identified

A new method of small target detection based on neural network

Science.gov (United States)

Hu, Jing; Hu, Yongli; Lu, Xinxin

2018-02-01

The detection and tracking of moving dim target in infrared image have been an research hotspot for many years. The target in each frame of images only occupies several pixels without any shape and structure information. Moreover, infrared small target is often submerged in complicated background with low signal-to-clutter ratio, making the detection very difficult. Different backgrounds exhibit different statistical properties, making it becomes extremely complex to detect the target. If the threshold segmentation is not reasonable, there may be more noise points in the final detection, which is unfavorable for the detection of the trajectory of the target. Single-frame target detection may not be able to obtain the desired target and cause high false alarm rate. We believe the combination of suspicious target detection spatially in each frame and temporal association for target tracking will increase reliability of tracking dim target. The detection of dim target is mainly divided into two parts, In the first part, we adopt bilateral filtering method in background suppression, after the threshold segmentation, the suspicious target in each frame are extracted, then we use LSTM(long short term memory) neural network to predict coordinates of target of the next frame. It is a brand-new method base on the movement characteristic of the target in sequence images which could respond to the changes in the relationship between past and future values of the values. Simulation results demonstrate proposed algorithm can effectively predict the trajectory of the moving small target and work efficiently and robustly with low false alarm.

Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image

Directory of Open Access Journals (Sweden)

Qi Dawei

2010-01-01

Full Text Available A modified Hopfield neural network with a novel cost function was presented for detecting wood defects boundary in the image. Different from traditional methods, the boundary detection problem in this paper was formulated as an optimization process that sought the boundary points to minimize a cost function. An initial boundary was estimated by Canny algorithm first. The pixel gray value was described as a neuron state of Hopfield neural network. The state updated till the cost function touches the minimum value. The designed cost function ensured that few neurons were activated except the neurons corresponding to actual boundary points and ensured that the activated neurons are positioned in the points which had greatest change in gray value. The tools of Matlab were used to implement the experiment. The results show that the noises of the image are effectively removed, and our method obtains more noiseless and vivid boundary than those of the traditional methods.

EVALUATION OF THE IMPORTANCE OF BIOCHEMICAL PARAMETERS IN RELATION TO ULTRASONOGRAPHIC FINDING IN ECTOPIC PREGNANCY DIAGNOSIS

Directory of Open Access Journals (Sweden)

Dragan Lončar

2011-09-01

Full Text Available The implantation of the fertilized egg outside the uterine cavity leads to the development of ectopic pregnancy. The incidence of ectopic pregnancy is 1/100 births. The most common place of ectopic implantation of the fertilized ovum is the oviduct (98% with predilection for the ampullar part of the Fallopian tube. The aim of this study was to determine the predictive importance of beta-hCG and progesterone concentration compared to ultrasonographic finding in the ectopic pregnancy diagnosis.We examined 24 patients with ectopic pregnancies which we divided according to the days of amenorrhea into two groups: the first group with the total of 28 patients from 16–42 days and another group of 8 patients with amenorrhea longer than 42 days. The control group was comprised of 20 patients with vital intrauterine pregnancy, gestational age of 42-52 days. Blood samples for quantitative determination of hormones were collected on three occasions after 48 hours in the forenoon time in the examined and control group of pregnant women. Ultrasonographic examinations of all pregnant women were carried out immediately after blood sampling, with the trans-vaginal approach using "make loop" option, and measurements with an accuracy of 0.1 mm.Mean values for beta-hCG range from 698-1774 mlU/ml in the first group of pregnant women, and in the second group of 1896 mlU/ml to 4410 mlU/ml with a statistically significant difference compared to the values in the control group (p <0.001. The concentration of progesterone in the first group of women ranging from 41-70 nmol/l, and in the second group of 76-94 nmol/l which is also the statistically significant difference compared to the control group (p<0.002. We have shown that ultrasonographic finding with its parameters reliably predicts the values of biochemical parameters both in normal intrauterine pregnancy and in the case of ectopic pregnancy.Embryo viability and implantation place condition the values of

Doppler-ultrasonographic finding of air in the portal vein: a case report

Energy Technology Data Exchange (ETDEWEB)

Park, Ki Soon; Lee, Kwan Sup; Lee, Yul; Chung, Soo Young; Bae, Sang Hoon [College of Medicine, Hallym University, Seoul (Korea, Republic of)

1994-03-15

Classically air in the portal vein has been detected on plain radiography, but computed tomography and ultrasonography have been shown to be more sensitive. We report a case of air in the PV in a 10-day-old infant with pneumatosis intestinalis with its ultrasonographic and Doppler findings. The patient was a 10-day-old infant born by cesarean section at 41 weeks. Simple abdomen film revealed branching pattern of radiolucent air shadows within in contour of liver, gas distention of bowel loops and thickenod bowel walls with lincar intraluminal air shadows in abdomen, suggesting necrotizing enterocolitis. So we performed Doppler ultrasonography. Ultrasonography showed branching pattern of hyperechogenic dots and along the lumen of left portal vein. The color Doppler study revealed an aliasing duo to increased velocity and whirling pattern of blood flow, and the Duplex Doppler spectral display showed sharp, vertical bidirectional spikes by air in portal vein. Air in the portal vein can be easily diagnosed by the following signs: hyperechogenic dots in the portal vein on ultrasonography and vertical, sharp bidirectional spikes superimposed on the usual Doppler tracing of the portal vein on Duplex ultrasonography.

Doppler-ultrasonographic finding of air in the portal vein: a case report

International Nuclear Information System (INIS)

Park, Ki Soon; Lee, Kwan Sup; Lee, Yul; Chung, Soo Young; Bae, Sang Hoon

1994-01-01

Classically air in the portal vein has been detected on plain radiography, but computed tomography and ultrasonography have been shown to be more sensitive. We report a case of air in the PV in a 10-day-old infant with pneumatosis intestinalis with its ultrasonographic and Doppler findings. The patient was a 10-day-old infant born by cesarean section at 41 weeks. Simple abdomen film revealed branching pattern of radiolucent air shadows within in contour of liver, gas distention of bowel loops and thickenod bowel walls with lincar intraluminal air shadows in abdomen, suggesting necrotizing enterocolitis. So we performed Doppler ultrasonography. Ultrasonography showed branching pattern of hyperechogenic dots and along the lumen of left portal vein. The color Doppler study revealed an aliasing duo to increased velocity and whirling pattern of blood flow, and the Duplex Doppler spectral display showed sharp, vertical bidirectional spikes by air in portal vein. Air in the portal vein can be easily diagnosed by the following signs: hyperechogenic dots in the portal vein on ultrasonography and vertical, sharp bidirectional spikes superimposed on the usual Doppler tracing of the portal vein on Duplex ultrasonography

A Recurrent Neural Network Approach to Rear Vehicle Detection Which Considered State Dependency

Directory of Open Access Journals (Sweden)

Kayichirou Inagaki

2003-08-01

Full Text Available Experimental vision-based detection often fails in cases when the acquired image quality is reduced by changing optical environments. In addition, the shape of vehicles in images that are taken from vision sensors change due to approaches by vehicle. Vehicle detection methods are required to perform successfully under these conditions. However, the conventional methods do not consider especially in rapidly varying by brightness conditions. We suggest a new detection method that compensates for those conditions in monocular vision-based vehicle detection. The suggested method employs a Recurrent Neural Network (RNN, which has been applied for spatiotemporal processing. The RNN is able to respond to consecutive scenes involving the target vehicle and can track the movements of the target by the effect of the past network states. The suggested method has a particularly beneficial effect in environments with sudden, extreme variations such as bright sunlight and shield. Finally, we demonstrate effectiveness by state-dependent of the RNN-based method by comparing its detection results with those of a Multi Layered Perceptron (MLP.

Non-proliferative diabetic retinopathy symptoms detection and classification using neural network.

Science.gov (United States)

Al-Jarrah, Mohammad A; Shatnawi, Hadeel

2017-08-01

Diabetic retinopathy (DR) causes blindness in the working age for people with diabetes in most countries. The increasing number of people with diabetes worldwide suggests that DR will continue to be major contributors to vision loss. Early detection of retinopathy progress in individuals with diabetes is critical for preventing visual loss. Non-proliferative DR (NPDR) is an early stage of DR. Moreover, NPDR can be classified into mild, moderate and severe. This paper proposes a novel morphology-based algorithm for detecting retinal lesions and classifying each case. First, the proposed algorithm detects the three DR lesions, namely haemorrhages, microaneurysms and exudates. Second, we defined and extracted a set of features from detected lesions. The set of selected feature emulates what physicians looked for in classifying NPDR case. Finally, we designed an artificial neural network (ANN) classifier with three layers to classify NPDR to normal, mild, moderate and severe. Bayesian regularisation and resilient backpropagation algorithms are used to train ANN. The accuracy for the proposed classifiers based on Bayesian regularisation and resilient backpropagation algorithms are 96.6 and 89.9, respectively. The obtained results are compared with results of the recent published classifier. Our proposed classifier outperforms the best in terms of sensitivity and specificity.

Detection of material property errors in handbooks and databases using artificial neural networks with hidden correlations

Science.gov (United States)

Zhang, Y. M.; Evans, J. R. G.; Yang, S. F.

2010-11-01

The authors have discovered a systematic, intelligent and potentially automatic method to detect errors in handbooks and stop their transmission using unrecognised relationships between materials properties. The scientific community relies on the veracity of scientific data in handbooks and databases, some of which have a long pedigree covering several decades. Although various outlier-detection procedures are employed to detect and, where appropriate, remove contaminated data, errors, which had not been discovered by established methods, were easily detected by our artificial neural network in tables of properties of the elements. We started using neural networks to discover unrecognised relationships between materials properties and quickly found that they were very good at finding inconsistencies in groups of data. They reveal variations from 10 to 900% in tables of property data for the elements and point out those that are most probably correct. Compared with the statistical method adopted by Ashby and co-workers [Proc. R. Soc. Lond. Ser. A 454 (1998) p. 1301, 1323], this method locates more inconsistencies and could be embedded in database software for automatic self-checking. We anticipate that our suggestion will be a starting point to deal with this basic problem that affects researchers in every field. The authors believe it may eventually moderate the current expectation that data field error rates will persist at between 1 and 5%.

The ultrasonographic changes of gallbladder wall and the corresponding hepatic pathology in patients with chronic viral hepatitis

International Nuclear Information System (INIS)

Zhang Haiying; Meng Fankun; Ding Huiguo

2006-01-01

Objective: To investigate the relationship between the ultrasonographic changes of gallbladder wall and the hepatic inflammation grading as well as fibrostic staging using ultrasound examination in patients with chronic viral hepatitis, and to survey the diagnostic standard for viral related cholecystitis. Methods: Five hundreds and nineteen chronic viral hepatitis patients and 104 normal control subjects were enrolled in the study. Ultrasound guided liver biopsy was performed in all patients with chronic viral hepatitis, in which the hepatic fibrostic stages(S) were divided into SI (n=148), S2(n=170), S3-4(n 201 ); and hepatic inflammation grades (G) were divided into G1 (n=124 ), G2 (n=204), G3 (n=191). The ultrasound scan was performed within 7 days after liver biopsy. The relationship between ultrasonographic changes of gallbladder and the hepatic inflammation grades and fibrostic stages were analyzed using statistic soft ware SPSS 11.5 for windows. Results: The percentage of the thickened gallbladder wall were 55%, 87%, 96% in S1, S2, S3-4, respectively, and were 45%, 82%, 95% in G1, G2, G3-4, respectively. Significant difference was revealed between G1 and G2, as well as between G2 and G3-4, with P < 0.05. Conclusion: A significant, positive correlative relationship exists between the ultrasonographic changes of gall-bladder wall and the hepatic inflammation grading and fibrostic staging in patients with chronic viral hepatitis. (authors)

An economic evaluation of second-trimester genetic ultrasonography for prenatal detection of down syndrome.

Science.gov (United States)

Vintzileos, A M; Ananth, C V; Fisher, A J; Smulian, J C; Day-Salvatore, D; Beazoglou, T; Knuppel, R A

1998-11-01

The objective of this study was to perform an economic evaluation of second-trimester genetic ultrasonography for prenatal detection of Down syndrome. More specifically, we sought to determine the following: (1) the diagnostic accuracy requirements (from the cost-benefit point of view) of genetic ultrasonography versus genetic amniocentesis for women at increased risk for fetal Down syndrome and (2) the possible economic impact of second-trimester genetic ultrasonography for the US population on the basis of the ultrasonographic accuracies reported in previously published studies. A cost-benefit equation was developed from the hypothesis that the cost of universal genetic amniocentesis of patients at increased risk for carrying a fetus with Down syndrome should be at least equal to the cost of universal genetic ultrasonography with amniocentesis used only for those with abnormal ultrasonographic results. The main components of the equation included the diagnostic accuracy of genetic ultrasonography (sensitivity and specificity for detecting Down syndrome), the costs of the amniocentesis package and genetic ultrasonography, and the lifetime cost of Down syndrome cases not detected by the genetic ultrasonography. After appropriate manipulation of the equation a graph was constructed, representing the balance between sensitivity and false-positive rate of genetic ultrasonography; this was used to examine the accuracy of previously published studies from the cost-benefit point of view. Sensitivity analyses included individual risks for Down syndrome ranging from 1:261 (risk of a 35-year-old at 18 weeks' gestation) to 1:44 (risk of a 44-year-old at 18 weeks' gestation). This economic evaluation was conducted from the societal perspective. Genetic ultrasonography was found to be economically beneficial only if the overall sensitivity for detecting Down syndrome was >74%. Even then, the cost-benefit ratio depended on the corresponding false-positive rate. Of the 7

The value of ultrasonographic examination in the evaluation of liver size and intrahepatic vessels; Wartosc badania ultrasonograficznego w ocenie wielkosci watroby i naczyn wewnatrzwatrobowych u psa i kotow

Energy Technology Data Exchange (ETDEWEB)

Narojek, T. [Szkola Glowna Gospodarstwa Wiejskiego, Warsaw (Poland)

1995-12-31

The objective of the investigations was to comparatively evaluate the size and shape of the liver on the basis of x-ray and ultrasonographic examinations and to estimate the liver parenchyma and intrahepatic vessels on the basis of ultrasonographic examinations. The studies were done on 64 dogs and 13 cats of different breed and sex, aged from 1 day to 18 years. The ultrasonographic examinations were done with the use of Bruel and Kjaer apparatus type 1849 and Concept 2000 by Dynamic Imaging. In 64 animals the ultrasonographic image of the liver was normal. In 7 cases an enlargement of the liver without any changes in the liver parenchyma was noted. An enlargement of intrahepatic veins was found in 8 cases (4 with circulatory insufficiency, 2 with ascites). The assessment of the size and shape of the liver done on that basis of ultrasound examinations agreed with that based on x-ray examinations. The ultrasonic examinations also allowed the evaluation of the liver parenchyma and intrahepatic veins. (author). 9 refs, figs, 3 tabs.

Detection of land cover change using an Artificial Neural Network on a time-series of MODIS satellite data

CSIR Research Space (South Africa)

Olivier, JC

2007-11-01

Full Text Available An Artificial Neural Network (ANN) is proposed to detect human-induced land cover change using a sliding window through a time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite surface reflectance pixel values. Training...

A Clinical Observation of Ultrasonographic Diagnosis on Right upper Quadrant Pain

International Nuclear Information System (INIS)

Lee, Suck Hong; Moon, T. Y.; Chang, H. Y.; Kim, B. S.

1982-01-01

The author analyzed a total of 127 cases of ultrasonography studied for evaluation of the causes of right upper quadrant pain during ten months from Feb.1 to Nov. 30 1981, at the department of radiology, Busan national university and St. Benedict hospital. The results were as follows: 1. ultrasonographic findings of the total of 127 cases are normal in 41(32.3%) cases, and abnormal in 86(67.6%) cases. 2. Clinical diagnosis of normal ultrasonographic cases is unknown in 15(36.6%) cases, hepatitis in 10(24.3%) cases, pancreatitis in 6(14.6%) case,enterocolitis in 5(12.1%) cases, acute gastritis in 3(7.5%), acute pyelonephritis in 1(2.4%) case, and clonorchiasis in 1(2.4%) case. 3. Pathological diagnosis of 50 cases out of 86 cases of abnormal ultrasonography is GB stone in 36(72.0%) cases, pancreatic cancer in 5(10.0%) cases, hepatoma in 3(6.0%) cases, CBD stone in 4(8.0%) cases, pancreatic pseudocyst in 1(12.9%) case and liver abscess in 1(2.0%) case. 4. Diagnostic accuracy of ultrasonography of GB stone was 91.7%, false positive 2.8% and false negative 6.6%. 5. Ultrasonography has the advantage of noninvasiveness and easy performance, but the diagnostic accuracy of ultrasonography alone was low. Ultrasonography is considered as a good screening and complementary method for evaluation of right upper quadrant pain

A Clinical Observation of Ultrasonographic Diagnosis on Right upper Quadrant Pain

Energy Technology Data Exchange (ETDEWEB)

Lee, Suck Hong; Moon, T. Y.; Chang, H. Y.; Kim, B. S. [Pusan National University College of Medicine, Busan (Korea, Republic of)

1982-12-15

The author analyzed a total of 127 cases of ultrasonography studied for evaluation of the causes of right upper quadrant pain during ten months from Feb.1 to Nov. 30 1981, at the department of radiology, Busan national university and St. Benedict hospital. The results were as follows: 1. ultrasonographic findings of the total of 127 cases are normal in 41(32.3%) cases, and abnormal in 86(67.6%) cases. 2. Clinical diagnosis of normal ultrasonographic cases is unknown in 15(36.6%) cases, hepatitis in 10(24.3%) cases, pancreatitis in 6(14.6%) case,enterocolitis in 5(12.1%) cases, acute gastritis in 3(7.5%), acute pyelonephritis in 1(2.4%) case, and clonorchiasis in 1(2.4%) case. 3. Pathological diagnosis of 50 cases out of 86 cases of abnormal ultrasonography is GB stone in 36(72.0%) cases, pancreatic cancer in 5(10.0%) cases, hepatoma in 3(6.0%) cases, CBD stone in 4(8.0%) cases, pancreatic pseudocyst in 1(12.9%) case and liver abscess in 1(2.0%) case. 4. Diagnostic accuracy of ultrasonography of GB stone was 91.7%, false positive 2.8% and false negative 6.6%. 5. Ultrasonography has the advantage of noninvasiveness and easy performance, but the diagnostic accuracy of ultrasonography alone was low. Ultrasonography is considered as a good screening and complementary method for evaluation of right upper quadrant pain

Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network

DEFF Research Database (Denmark)

Dyrmann, Mads; Nyholm Jørgensen, Rasmus; Midtiby, Henrik Skov

2017-01-01

This pap er presents a metho d for au tomating weed detectio n in colour images despite heavy lea f occlusion. A fully convolu tio n al neural network is used to detect the weed s. The netwo rk is trained and validated on a tot al of more than 17,000 ann otations of w eeds in images from wint er w...

Spot detection in microscopy images using Convolutional Neural Network with sliding-window approach

CSIR Research Space (South Africa)

Mabaso, Matsilele A

2018-01-01

Full Text Available stream_source_info Mabaso_20271_2018.pdf.txt stream_content_type text/plain stream_size 24351 Content-Encoding UTF-8 stream_name Mabaso_20271_2018.pdf.txt Content-Type text/plain; charset=UTF-8 Spot Detection....n. Krizhevsky, A., Sutskever, I. & Hinton, G. E., 2012. Imagenet classication with deep convolutional neural networks. s.l., s.n., pp. 1-9. Li, R. et al., 2014. Deep learning based imaging data completion for improved brain disease diagnosis. Quebec City, s...

A convolutional neural network for intracranial hemorrhage detection in non-contrast CT

Science.gov (United States)

Patel, Ajay; Manniesing, Rashindra

2018-02-01

The assessment of the presence of intracranial hemorrhage is a crucial step in the work-up of patients requiring emergency care. Fast and accurate detection of intracranial hemorrhage can aid treating physicians by not only expediting and guiding diagnosis, but also supporting choices for secondary imaging, treatment and intervention. However, the automatic detection of intracranial hemorrhage is complicated by the variation in appearance on non-contrast CT images as a result of differences in etiology and location. We propose a method using a convolutional neural network (CNN) for the automatic detection of intracranial hemorrhage. The method is trained on a dataset comprised of cerebral CT studies for which the presence of hemorrhage has been labeled for each axial slice. A separate test dataset of 20 images is used for quantitative evaluation and shows a sensitivity of 0.87, specificity of 0.97 and accuracy of 0.95. The average processing time for a single three-dimensional (3D) CT volume was 2.7 seconds. The proposed method is capable of fast and automated detection of intracranial hemorrhages in non-contrast CT without being limited to a specific subtype of pathology.

Pneumothorax detection in chest radiographs using convolutional neural networks

Science.gov (United States)

Blumenfeld, Aviel; Konen, Eli; Greenspan, Hayit

2018-02-01

This study presents a computer assisted diagnosis system for the detection of pneumothorax (PTX) in chest radiographs based on a convolutional neural network (CNN) for pixel classification. Using a pixel classification approach allows utilization of the texture information in the local environment of each pixel while training a CNN model on millions of training patches extracted from a relatively small dataset. The proposed system uses a pre-processing step of lung field segmentation to overcome the large variability in the input images coming from a variety of imaging sources and protocols. Using a CNN classification, suspected pixel candidates are extracted within each lung segment. A postprocessing step follows to remove non-physiological suspected regions and noisy connected components. The overall percentage of suspected PTX area was used as a robust global decision for the presence of PTX in each lung. The system was trained on a set of 117 chest x-ray images with ground truth segmentations of the PTX regions. The system was tested on a set of 86 images and reached diagnosis accuracy of AUC=0.95. Overall preliminary results are promising and indicate the growing ability of CAD based systems to detect findings in medical imaging on a clinical level accuracy.

Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

Science.gov (United States)

Abdeljaber, Osama; Avci, Onur; Kiranyaz, Serkan; Gabbouj, Moncef; Inman, Daniel J.

2017-02-01

Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous damage detection has always been one of the principal objectives of SHM applications. The performance of a classical damage detection system predominantly depends on the choice of the features and the classifier. While the fixed and hand-crafted features may either be a sub-optimal choice for a particular structure or fail to achieve the same level of performance on another structure, they usually require a large computation power which may hinder their usage for real-time structural damage detection. This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body. The proposed method performs vibration-based damage detection and localization of the damage in real-time. The advantage of this approach is its ability to extract optimal damage-sensitive features automatically from the raw acceleration signals. Large-scale experiments conducted on a grandstand simulator revealed an outstanding performance and verified the computational efficiency of the proposed real-time damage detection method.

Using recurrent neural network models for early detection of heart failure onset.

Science.gov (United States)

Choi, Edward; Schuetz, Andy; Stewart, Walter F; Sun, Jimeng

2017-03-01

We explored whether use of deep learning to model temporal relations among events in electronic health records (EHRs) would improve model performance in predicting initial diagnosis of heart failure (HF) compared to conventional methods that ignore temporality. Data were from a health system's EHR on 3884 incident HF cases and 28 903 controls, identified as primary care patients, between May 16, 2000, and May 23, 2013. Recurrent neural network (RNN) models using gated recurrent units (GRUs) were adapted to detect relations among time-stamped events (eg, disease diagnosis, medication orders, procedure orders, etc.) with a 12- to 18-month observation window of cases and controls. Model performance metrics were compared to regularized logistic regression, neural network, support vector machine, and K-nearest neighbor classifier approaches. Using a 12-month observation window, the area under the curve (AUC) for the RNN model was 0.777, compared to AUCs for logistic regression (0.747), multilayer perceptron (MLP) with 1 hidden layer (0.765), support vector machine (SVM) (0.743), and K-nearest neighbor (KNN) (0.730). When using an 18-month observation window, the AUC for the RNN model increased to 0.883 and was significantly higher than the 0.834 AUC for the best of the baseline methods (MLP). Deep learning models adapted to leverage temporal relations appear to improve performance of models for detection of incident heart failure with a short observation window of 12-18 months. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Neural Correlates of Temporal Complexity and Synchrony during Audiovisual Correspondence Detection.

Science.gov (United States)

Baumann, Oliver; Vromen, Joyce M G; Cheung, Allen; McFadyen, Jessica; Ren, Yudan; Guo, Christine C

2018-01-01

We often perceive real-life objects as multisensory cues through space and time. A key challenge for audiovisual integration is to match neural signals that not only originate from different sensory modalities but also that typically reach the observer at slightly different times. In humans, complex, unpredictable audiovisual streams lead to higher levels of perceptual coherence than predictable, rhythmic streams. In addition, perceptual coherence for complex signals seems less affected by increased asynchrony between visual and auditory modalities than for simple signals. Here, we used functional magnetic resonance imaging to determine the human neural correlates of audiovisual signals with different levels of temporal complexity and synchrony. Our study demonstrated that greater perceptual asynchrony and lower signal complexity impaired performance in an audiovisual coherence-matching task. Differences in asynchrony and complexity were also underpinned by a partially different set of brain regions. In particular, our results suggest that, while regions in the dorsolateral prefrontal cortex (DLPFC) were modulated by differences in memory load due to stimulus asynchrony, areas traditionally thought to be involved in speech production and recognition, such as the inferior frontal and superior temporal cortex, were modulated by the temporal complexity of the audiovisual signals. Our results, therefore, indicate specific processing roles for different subregions of the fronto-temporal cortex during audiovisual coherence detection.

Proposed Network Intrusion Detection System ‎In Cloud Environment Based on Back ‎Propagation Neural Network

Directory of Open Access Journals (Sweden)

Shawq Malik Mehibs

2017-12-01

Full Text Available Cloud computing is distributed architecture, providing computing facilities and storage resource as a service over the internet. This low-cost service fulfills the basic requirements of users. Because of the open nature and services introduced by cloud computing intruders impersonate legitimate users and misuse cloud resource and services. To detect intruders and suspicious activities in and around the cloud computing environment, intrusion detection system used to discover the illegitimate users and suspicious action by monitors different user activities on the network .this work proposed based back propagation artificial neural network to construct t network intrusion detection in the cloud environment. The proposed module evaluated with kdd99 dataset the experimental results shows promising approach to detect attack with high detection rate and low false alarm rate

Ultrasonographic wall thickness measurement of the upper and lower uterine segments in the prediction of the progress of preterm labour.

Science.gov (United States)

Sayed Ahmed, W A; Madny, E H; Habash, Y H; Ibrahim, Z M; Morsy, A G K; Said, M E

2015-01-01

To assess the role of ultrasonographic measurement of the upper and lower uterine segments wall thickness in predicting the progress of preterm labour in patients presenting with preterm labour pains. Fifty pregnant women presenting at Obstetrics Department - Suez Canal University, Egypt with regular lower abdominal pains and diagnosed as having preterm labour were enrolled in the study. Measurements of the upper and lower uterine segments wall thickness by transabdominal ultrasonography in-between contractions and with full bladder were taken. The upper/lower uterine wall thickness ratio was calculated and correlated to the progress of the preterm labour and to the response to tocolytics. The ultrasonographic upper/lower uterine wall thickness ratio was directly related to the progress of preterm delivery (PTD). The change in this ratio is correlated inversely with the response to tocolysis. Using the ROC curve, when the upper/lower uterine wall thickness ratio was ≤ 1.26 the sensitivity was 94.74 and the specificity was 100.00, and when the ratio was ≤ 1.52 the sensitivity was 100.00 and the specificity was 83.33. These data may serve as a baseline ultrasonographic reference values for further studies in prediction the progress of preterm labour in patients presenting with preterm labour pains.

Neural networks for oil spill detection using TerraSAR-X data

Science.gov (United States)

Avezzano, Ruggero G.; Velotto, Domenico; Soccorsi, Matteo; Del Frate, Fabio; Lehner, Susanne

2011-11-01

The increased amount of available Synthetic Aperture Radar (SAR) images involves a growing workload on the operators at analysis centers. In addition, even if the operators go through extensive training to learn manual oil spill detection, they can provide different and subjective responses. Hence, the upgrade and improvements of algorithms for automatic detection that can help in screening the images and prioritizing the alarms are of great benefit. In this paper we present the potentialities of TerraSAR-X (TS-X) data and Neural Network algorithms for oil spills detection. The radar on board satellite TS-X provides X-band images with a resolution of up to 1m. Such resolution can be very effective in the monitoring of coastal areas to prevent sea oil pollution. The network input is a vector containing the values of a set of features characterizing an oil spill candidate. The network output gives the probability for the candidate to be a real oil spill. Candidates with a probability less than 50% are classified as look-alikes. The overall classification performances have been evaluated on a data set of 50 TS-X images containing more than 150 examples of certified oil spills and well-known look-alikes (e.g. low wind areas, wind shadows, biogenic films). The preliminary classification results are satisfactory with an overall detection accuracy above 80%.

Applying long short-term memory recurrent neural networks to intrusion detection

Directory of Open Access Journals (Sweden)

Ralf C. Staudemeyer

2015-07-01

Full Text Available We claim that modelling network traffic as a time series with a supervised learning approach, using known genuine and malicious behaviour, improves intrusion detection. To substantiate this, we trained long short-term memory (LSTM recurrent neural networks with the training data provided by the DARPA / KDD Cup ’99 challenge. To identify suitable LSTM-RNN network parameters and structure we experimented with various network topologies. We found networks with four memory blocks containing two cells each offer a good compromise between computational cost and detection performance. We applied forget gates and shortcut connections respectively. A learning rate of 0.1 and up to 1,000 epochs showed good results. We tested the performance on all features and on extracted minimal feature sets respectively. We evaluated different feature sets for the detection of all attacks within one network and also to train networks specialised on individual attack classes. Our results show that the LSTM classifier provides superior performance in comparison to results previously published results of strong static classifiers. With 93.82% accuracy and 22.13 cost, LSTM outperforms the winning entries of the KDD Cup ’99 challenge by far. This is due to the fact that LSTM learns to look back in time and correlate consecutive connection records. For the first time ever, we have demonstrated the usefulness of LSTM networks to intrusion detection.

A research about breast cancer detection using different neural networks and K-MICA algorithm

Directory of Open Access Journals (Sweden)

A A Kalteh

2013-01-01

Full Text Available Breast cancer is the second leading cause of death for women all over the world. The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. This paper presents a novel hybrid intelligent method for detection of breast cancer. The proposed method includes two main modules: Clustering module and the classifier module. In the clustering module, first the input data will be clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA and K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks and the radial basis function neural networks are investigated. Using the experimental study, we choose the best classifier in order to recognize the breast cancer. The proposed system is tested on Wisconsin Breast Cancer (WBC database and the simulation results show that the recommended system has high accuracy.

Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

Yi Zhang

2017-04-01

Full Text Available Ear detection is an important step in ear recognition approaches. Most existing ear detection techniques are based on manually designing features or shallow learning algorithms. However, researchers found that the pose variation, occlusion, and imaging conditions provide a great challenge to the traditional ear detection methods under uncontrolled conditions. This paper proposes an efficient technique involving Multiple Scale Faster Region-based Convolutional Neural Networks (Faster R-CNN to detect ears from 2D profile images in natural images automatically. Firstly, three regions of different scales are detected to infer the information about the ear location context within the image. Then an ear region filtering approach is proposed to extract the correct ear region and eliminate the false positives automatically. In an experiment with a test set of 200 web images (with variable photographic conditions, 98% of ears were accurately detected. Experiments were likewise conducted on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2 and University of Beira Interior Ear dataset (UBEAR, which contain large occlusion, scale, and pose variations. Detection rates of 100% and 98.22%, respectively, demonstrate the effectiveness of the proposed approach.

Attacks and Intrusion Detection in Cloud Computing Using Neural Networks and Particle Swarm Optimization Algorithms

Directory of Open Access Journals (Sweden)

Ahmad Shokuh Saljoughi

2018-01-01

Full Text Available Today, cloud computing has become popular among users in organizations and companies. Security and efficiency are the two major issues facing cloud service providers and their customers. Since cloud computing is a virtual pool of resources provided in an open environment (Internet, cloud-based services entail security risks. Detection of intrusions and attacks through unauthorized users is one of the biggest challenges for both cloud service providers and cloud users. In the present study, artificial intelligence techniques, e.g. MLP Neural Network sand particle swarm optimization algorithm, were used to detect intrusion and attacks. The methods were tested for NSL-KDD, KDD-CUP datasets. The results showed improved accuracy in detecting attacks and intrusions by unauthorized users.

Human prenatal diagnosis

International Nuclear Information System (INIS)

Filkins, K.; Russo, R.J.

1985-01-01

The multiauthor text is written as a ''guide to rationalize and clarify certain aspects of diagnosis, general counseling and intervention'' for ''health professionals who provide care to pregnant women.'' The text is not aimed at the ultrasonographer but rather at the physicians who are clinically responsible for patient management. Chapters of relevance to radiologists include an overview of prenatal screening and counseling, diagnosis of neural tube defects, ultrasonographic (US) scanning of fetal disorders in the first and second trimesters of pregnancy, US scanning in the third trimester, multiple gestation and selective termination, fetal echo and Doppler studies, and fetal therapy. Also included are overviews of virtually all currently utilized prenatal diagnostic techniques including amniocentesis, fetal blood sampling, fetoscopy, recombinant DNA detection of hemoglobinopathies, chorionic villus sampling, embryoscopy, legal issues, and diagnosis of Mendelian disorders by DNA analysis

Ultrasonographic description of canine mastitis.

Science.gov (United States)

Trasch, Katja; Wehrend, Axel; Bostedt, Hartwig

2007-01-01

Ultrasonographic images were acquired of the mammary glands of 40 bitches with physiologically lactating (n = 20) or inflamed glands (n = 20). Echogenicity, structure, homogeneity, thickness, and distinguishability of each tissue layer were assessed. Additionally, overall echogenicity was noted. In the normal lactating gland, different tissues could be differentiated easily. The parenchyma was, without exception, separated from adjacent tissues and was visible as medium echogenic tissue with a coarse-grained structure. The tissue always had some echogenic lines and anechoic areas and was slightly heterogeneous. The loss of distinct layering of the tissue was characteristic of an inflamed mammary gland and inflamed regions had reduced echogenicity. Additionally in five bitches with mastitis, the ultrasound examination was repeated five times for documentation of the progress of the illness and associated changes, supplemented with a color Doppler sonogram to assess changes in blood vessel density. Information from the examinations carried out via B-mode did not allow treatment success to be predicted. Two bitches with reduced blood vessel density centrally had a poor outcome whereas three bitches with increased blood vessel density had a good outcome. Thus, Doppler sonography might be a useful tool to obtain information of the prognosis in acute canine mastitis.

Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network.

Science.gov (United States)

Wang, Zhiwei; Liu, Chaoyue; Cheng, Danpeng; Wang, Liang; Yang, Xin; Cheng, Kwang-Ting

2018-05-01

Automated methods for detecting clinically significant (CS) prostate cancer (PCa) in multi-parameter magnetic resonance images (mp-MRI) are of high demand. Existing methods typically employ several separate steps, each of which is optimized individually without considering the error tolerance of other steps. As a result, they could either involve unnecessary computational cost or suffer from errors accumulated over steps. In this paper, we present an automated CS PCa detection system, where all steps are optimized jointly in an end-to-end trainable deep neural network. The proposed neural network consists of concatenated subnets: 1) a novel tissue deformation network (TDN) for automated prostate detection and multimodal registration and 2) a dual-path convolutional neural network (CNN) for CS PCa detection. Three types of loss functions, i.e., classification loss, inconsistency loss, and overlap loss, are employed for optimizing all parameters of the proposed TDN and CNN. In the training phase, the two nets mutually affect each other and effectively guide registration and extraction of representative CS PCa-relevant features to achieve results with sufficient accuracy. The entire network is trained in a weakly supervised manner by providing only image-level annotations (i.e., presence/absence of PCa) without exact priors of lesions' locations. Compared with most existing systems which require supervised labels, e.g., manual delineation of PCa lesions, it is much more convenient for clinical usage. Comprehensive evaluation based on fivefold cross validation using 360 patient data demonstrates that our system achieves a high accuracy for CS PCa detection, i.e., a sensitivity of 0.6374 and 0.8978 at 0.1 and 1 false positives per normal/benign patient.

Automatic construction of a recurrent neural network based classifier for vehicle passage detection

Science.gov (United States)

Burnaev, Evgeny; Koptelov, Ivan; Novikov, German; Khanipov, Timur

2017-03-01

Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

Mammographic and Ultrasonographic Findings of the Chemoport Insertion Site

International Nuclear Information System (INIS)

Kim, Seun Jung; Kang, Bong Joo; Cha, Eun Suk; Park, Hye Jung; Kim, Sung Hun; Choi, Jae Jeong; Lee, Ji Hye

2010-01-01

To describe mammographic and ultrasonographic findings of previous chemoport insertion sites. We included patients who had abnormal findings at chemoport insertion sites on mammography and ultrasonography from 224 patients who underwent chemoport insertion and breast imaging at our institution between January, 2005, and December, 2007. Abnormal findings were identified in 16 mammographies and 14 ultrasonographies in 10 patients. The mean age was 50.9 years and the age range was from 44 to 67 years. Abnormal findings on mammography and ultrasonography were retrospectively analyzed according to ACR/BI-RADS. All cases were followed up with imaging studies for 2 years to confirm changes after chemoport insertion. Of the abnormal findings identified on mammography, focal asymmetry (7/16) was the most common. Other abnormal findings included mass (6/16), skin retraction (2/16), residual chemoport tip (1/16), and trabecular thickening (1/16). Of the abnormal findings seen on ultrasonography, skin thickening (12/14) was the most common. Other abnormal findings included mass (5/14), diffuse increased echogenicity of subcutaneous tissue (1/14), and a localized skin nodule (1/14). Abnormal findings on mammography and ultrasonography were located in the upper outer quadrant in 5 patients, upper inner quadrant in 3 patients, and mid upper portion in 1 patient. In 1 patient, the abnormal finding was only identified in the mediolateral oblique view of her mammography. Radiologists should be aware of potential abnormal findings on mammography and ultrasonography following chemoport insertion. In particular, ultrasonography is a very useful modality for detecting skin complications after chemoport insertion

Ultrasonographic characteristics and BI-RADS-US classification of BRCA1 mutation-associated breast cancer in Guangxi, China.

Science.gov (United States)

Li, Cheng; Liu, Junjie; Wang, Sida; Chen, Yuanyuan; Yuan, Zhigang; Zeng, Jian; Li, Zhixian

2015-01-01

To retrospectively analyze and compare the ultrasonographic characteristics and BI-RADS-US classification between patients with BRCA1 mutation-associated breast cancer and those without BRCA1 gene mutation in Guangxi, China. The study was performed in 36 lesions from 34 BRCA1 mutation-associated breast cancer patients. A total of 422 lesions from 422 breast cancer patients without BRCA1 mutations served as control group. The comparison of the ultrasonographic features and BI-RADS-US classification between two the groups were reviewed. More complex inner echo was disclosed in BRCA1 mutation-associated breast cancer patients (x(2) = 4.741, P = 0.029). The BI-RADS classification of BRCA1 mutation-associated breast cancer was lower (U = 6094.0, P = 0.022). BRCA1 mutation-associated breast cancer frequently displays as microlobulated margin and complex echo. It also shows more benign characteristics in morphology, and the BI-RADS classification is prone to be underestimated.

Single Layer Recurrent Neural Network for detection of swarm-like earthquakes in W-Bohemia/Vogtland - the method

Czech Academy of Sciences Publication Activity Database

Doubravová, Jana; Wiszniowski, J.; Horálek, Josef

2016-01-01

Roč. 93, August (2016), s. 138-149 ISSN 0098-3004 R&D Projects: GA ČR GAP210/12/2336; GA MŠk LM2010008 Institutional support: RVO:67985530 Keywords : event detection * artificial neural network * West Bohemia/Vogtland Subject RIV: DC - Siesmology, Volcanology, Earth Structure Impact factor: 2.533, year: 2016

DETECTION OF CLAMPING FORCES ON MOUNTING A CONSTRUCTION VIA NEURAL NETWORK FOR THE FINITE-ELEMENT MODEL OF COMPRESSOR-CONDENSING UNIT

Directory of Open Access Journals (Sweden)

S. V. Krasnovskaya

2017-01-01

Full Text Available The article provides a brief review of a condensing unit and problems of mathematic simulation. It examines the influence of pretension on the strain-stress state of a construction by means of finiteelement modeling. The arrangement of a set of input-output data for neural network is also considered. The article investigates a possibility to predict mounting precision via neural networks; by analogy with the above calculations it examines the option to detect clamping forces on mounting compressorcondensing unit. 

The neural circuits of innate fear: detection, integration, action, and memorization

Science.gov (United States)

Silva, Bianca A.; Gross, Cornelius T.

2016-01-01

How fear is represented in the brain has generated a lot of research attention, not only because fear increases the chances for survival when appropriately expressed but also because it can lead to anxiety and stress-related disorders when inadequately processed. In this review, we summarize recent progress in the understanding of the neural circuits processing innate fear in rodents. We propose that these circuits are contained within three main functional units in the brain: a detection unit, responsible for gathering sensory information signaling the presence of a threat; an integration unit, responsible for incorporating the various sensory information and recruiting downstream effectors; and an output unit, in charge of initiating appropriate bodily and behavioral responses to the threatful stimulus. In parallel, the experience of innate fear also instructs a learning process leading to the memorization of the fearful event. Interestingly, while the detection, integration, and output units processing acute fear responses to different threats tend to be harbored in distinct brain circuits, memory encoding of these threats seems to rely on a shared learning system. PMID:27634145

Ultrasonographic differentiation between Kikuchi's disease and lymphoma in patients with cervical lymphadenopathy

International Nuclear Information System (INIS)

Lo, Wu-Chia; Chang, Wen-Cheng; Lin, Yu-Chin; Hsu, Yao-Peng; Liao, Li-Jen

2012-01-01

Purpose: Kikuchi's disease, or histiocytic necrotizing lymphadenitis, is a self-limited necrotizing lymphadenitis. Clinically, it resembles lymphoma. We want to compare the sonographic features between Kikuchi's disease and lymphoma in patients with cervical lymphadenopathy. Materials and methods: The study protocol was approved by the institutional review board. Two hundred and twenty six cervical lymph nodes (137 nodes from 21 Kikuchi's disease patients and 89 nodes from 20 malignant lymphoma patients) were examined. The demographic and ultrasonographic characteristics of lymph nodes were collected and analyzed. Results: The Kikuchi's disease patients (mean age, 24.2 years; range, 8–57 years) were younger than those with lymphoma (mean age, 54 years; range, 13–81 years). There was no difference in laterality of nodes (p = 0.19). The nodal distribution demonstrated most enlarged neck lymph nodes located at level II, III and V. The ranges of short-axis and long-axis length were 6.5 ± 2.3 mm (mean ± SD) versus 13.4 ± 5.1 mm and 13.4 ± 5.0 mm versus 21.2 ± 9.2 mm for Kikuchi's disease versus lymphoma (p 0.05). Conclusion: Analysis of basic ultrasonographic characteristics (size, shape, rims, matting and echotexture) helps differentiate cervical lymph nodes in patients with Kikuchi's disease and lymphoma. Cervical lymphadenopathies in patients with Kikuchi's disease have smaller size, less round, less micronodular reticular echotexture, and more signs of matting and cortical widening than those with lymphoma examined under ultrasound.

Human Detection System by Fusing Depth Map-Based Method and Convolutional Neural Network-Based Method

Directory of Open Access Journals (Sweden)

Anh Vu Le

2017-01-01

Full Text Available In this paper, the depth images and the colour images provided by Kinect sensors are used to enhance the accuracy of human detection. The depth-based human detection method is fast but less accurate. On the other hand, the faster region convolutional neural network-based human detection method is accurate but requires a rather complex hardware configuration. To simultaneously leverage the advantages and relieve the drawbacks of each method, one master and one client system is proposed. The final goal is to make a novel Robot Operation System (ROS-based Perception Sensor Network (PSN system, which is more accurate and ready for the real time application. The experimental results demonstrate the outperforming of the proposed method compared with other conventional methods in the challenging scenarios.

Diabetic retinopathy screening using deep neural network.

Science.gov (United States)

Ramachandran, Nishanthan; Hong, Sheng Chiong; Sime, Mary J; Wilson, Graham A

2017-09-07

There is a burgeoning interest in the use of deep neural network in diabetic retinal screening. To determine whether a deep neural network could satisfactorily detect diabetic retinopathy that requires referral to an ophthalmologist from a local diabetic retinal screening programme and an international database. Retrospective audit. Diabetic retinal photos from Otago database photographed during October 2016 (485 photos), and 1200 photos from Messidor international database. Receiver operating characteristic curve to illustrate the ability of a deep neural network to identify referable diabetic retinopathy (moderate or worse diabetic retinopathy or exudates within one disc diameter of the fovea). Area under the receiver operating characteristic curve, sensitivity and specificity. For detecting referable diabetic retinopathy, the deep neural network had an area under receiver operating characteristic curve of 0.901 (95% confidence interval 0.807-0.995), with 84.6% sensitivity and 79.7% specificity for Otago and 0.980 (95% confidence interval 0.973-0.986), with 96.0% sensitivity and 90.0% specificity for Messidor. This study has shown that a deep neural network can detect referable diabetic retinopathy with sensitivities and specificities close to or better than 80% from both an international and a domestic (New Zealand) database. We believe that deep neural networks can be integrated into community screening once they can successfully detect both diabetic retinopathy and diabetic macular oedema. © 2017 Royal Australian and New Zealand College of Ophthalmologists.

Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network.

Science.gov (United States)

An, Quanzhi; Pan, Zongxu; You, Hongjian

2018-01-24

Target detection is one of the important applications in the field of remote sensing. The Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) satellite launched by China is a powerful tool for maritime monitoring. This work aims at detecting ships in GF-3 SAR images using a new land masking strategy, the appropriate model for sea clutter and a neural network as the discrimination scheme. Firstly, the fully convolutional network (FCN) is applied to separate the sea from the land. Then, by analyzing the sea clutter distribution in GF-3 SAR images, we choose the probability distribution model of Constant False Alarm Rate (CFAR) detector from K-distribution, Gamma distribution and Rayleigh distribution based on a tradeoff between the sea clutter modeling accuracy and the computational complexity. Furthermore, in order to better implement CFAR detection, we also use truncated statistic (TS) as a preprocessing scheme and iterative censoring scheme (ICS) for boosting the performance of detector. Finally, we employ a neural network to re-examine the results as the discrimination stage. Experiment results on three GF-3 SAR images verify the effectiveness and efficiency of this approach.

The Ultrasonographic Findings of Trigger Points of Myofascial Pain Syndrome in a Rabbit Model

International Nuclear Information System (INIS)

Moon, Kyung Mi; Park, Seog Hee; Lee, Sang Heon; Kim, Joo Hyun; Kim, Han Kyum

2005-01-01

Myofascial pain syndrome (MPS) is a common cause of musculoskeletal pain. Myofascial trigger points (MTrPs) have been repeatedly described by numerous authors. However, there have been few studies in which their existence and behavior was supported and their location confirmed. The purpose of this study was to determine whether diagnostic ultrasonography is an objective diagnostic tool which is able to significantly identify or detect the soft tissue changes in the region of clinically identified active MTrPs by using a rabbit experimental model. Ten MPS model rabbits were used in this study. We made an MPS animal model by causing the rabbits to overuse one leg for 3 weeks by cutting the contralateral L4 spinal nerve root. We compared the ultrasonographic findings of the taut band at pre-OP with those at post-OP during the consecutive three week period. To find the taut bands of the muscle, after skin exposure, the muscles were gently rubbed or pinched with the thumb and index finger on the two opposing surfaces of the muscle across the direction of the fibers. Then, the muscle was held in the same way, but with a 5-8 MHz stick probe being used in place of the thumb. After the palpation of various muscles, we selected the hardest and largest myofascial trigger nodule, in order to observe the ultrasonographic and power Doppler findings of the MPS. The size, shape, echogenecity and vascularity of the MTrPs were observed. The analysis of the results of the ultrasonography revealed that all MTrPs have a hyperechoic area. The mean thickness of the hyperechoic lesion in the biceps was 0.96±0.14 cm in the MPS site (at pre-OP?), and 0.49±0.12 cm at post-OP 3weeks (p < 0.01). The hyperechoic lesions in all of the studied biceps femoris of the rabbits were observed by high resolution ultrasonography. No definitively decreased vascularity was observed within the hyperechoic area by power Doppler imaging. Until now, there has been no objective method for the diagnosis of MPS

Subject independent facial expression recognition with robust face detection using a convolutional neural network.

Science.gov (United States)

Matsugu, Masakazu; Mori, Katsuhiko; Mitari, Yusuke; Kaneda, Yuji

2003-01-01

Reliable detection of ordinary facial expressions (e.g. smile) despite the variability among individuals as well as face appearance is an important step toward the realization of perceptual user interface with autonomous perception of persons. We describe a rule-based algorithm for robust facial expression recognition combined with robust face detection using a convolutional neural network. In this study, we address the problem of subject independence as well as translation, rotation, and scale invariance in the recognition of facial expression. The result shows reliable detection of smiles with recognition rate of 97.6% for 5600 still images of more than 10 subjects. The proposed algorithm demonstrated the ability to discriminate smiling from talking based on the saliency score obtained from voting visual cues. To the best of our knowledge, it is the first facial expression recognition model with the property of subject independence combined with robustness to variability in facial appearance.

Detection of Atrial Fibrillation Using Artifical Neural Network with Power Spectrum Density of RR Interval of Electrocardiogram

Science.gov (United States)

Afdala, Adfal; Nuryani, Nuryani; Satrio Nugroho, Anto

2017-01-01

Atrial fibrillation (AF) is a disorder of the heart with fairly high mortality in adults. AF is a common heart arrythmia which is characterized by a missing or irregular contraction of atria. Therefore, finding a method to detect atrial fibrillation is necessary. In this article a system to detect atrial fibrillation has been proposed. Detection system utilized backpropagation artifical neural network. Data input in this method includes power spectrum density of R-peaks interval of electrocardiogram which is selected by wrapping method. This research uses parameter learning rate, momentum, epoch and hidden layer. System produces good performance with accuracy, sensitivity, and specificity of 83.55%, 86.72 % and 81.47 %, respectively.

A hybrid neural network – world cup optimization algorithm for melanoma detection

Directory of Open Access Journals (Sweden)

Razmjooy Navid

2018-03-01

Full Text Available One of the most dangerous cancers in humans is Melanoma. However, early detection of melanoma can help us to cure it completely. This paper presents a new efficient method to detect malignancy in melanoma via images. At first, the extra scales are eliminated by using edge detection and smoothing. Afterwards, the proposed method can be utilized to segment the cancer images. Finally, the extra information is eliminated by morphological operations and used to focus on the area which melanoma boundary potentially exists. To do this, World Cup Optimization algorithm is utilized to optimize an MLP neural Networks (ANN. World Cup Optimization algorithm is a new meta-heuristic algorithm which is recently presented and has a good performance in some optimization problems. WCO is a derivative-free, Meta-Heuristic algorithm, mimicking the world’s FIFA competitions. World cup Optimization algorithm is a global search algorithm while gradient-based back propagation method is local search. In this proposed algorithm, multi-layer perceptron network (MLP employs the problem’s constraints and WCO algorithm attempts to minimize the root mean square error. Experimental results show that the proposed method can develop the performance of the standard MLP algorithm significantly.

Abdominal ultrasonographic findings associated with feline infectious peritonitis: a retrospective review of 16 cases.

Science.gov (United States)

Lewis, Kristin M; O'Brien, Robert T

2010-01-01

The feline infectious peritonitis virus (FIPV) is a mutated form of the feline enteric coronavirus (FeCV) that can present with a variety of clinical signs. The purpose of this retrospective study was to analyze abdominal ultrasonographic findings associated with cats with confirmed FIPV infection. Sixteen cases were included in the study from a review of medical records at two academic institutions; inclusion was based either on necropsy lesions (n=13) or a combination of histopathological, cytological, and clinicopathological findings highly suggestive of FIPV infection (n=3). The liver was judged to be normal in echogenicity in 11 (69%) cats, diffusely hypoechoic in three cats, focally hyperechoic in one cat, and focally hypoechoic in one cat. Five cats had a hypoechoic subcapsular rim in one (n=3) or both (n=2) kidneys. Free fluid was present in the peritoneal cavity in seven cats and in the retroperitoneal space in one cat. Abdominal lymphadenopathy was noted in nine cats. The spleen was normal in echogenicity in 14 cats and was hypoechoic in two. One cat had bilateral orchitis with loss of normal testicular architecture. Although none of these ultrasonographic findings are specific for FIPV infection, a combination of these findings should increase the index of suspicion for FIPV infection when considered along with appropriate clinical signs.

Prenatal color Doppler ultrasonographic diagnosis of fetal tetralogy of Fallot

International Nuclear Information System (INIS)

Tan Buqiao

2009-01-01

Objective: To investigate the sonographic findings of tetralogy of Fallot in fetuses. Methods: The data of color Doppler ultrasonography and follow-up results of 5 fetal tetralogy of Fallot were analyzed retrospectively, and their abnormal ultrasound imaging characteristic were summarized. Results: Two cases were proved tetralogy of Fallot by autopsy, and three cases were confirmed to be tetralogy of Fallot by echocardiography after birth. The image features were the main aorta situated above the ventricular septal defect, pulmonary stenosis, no obvious thickening of the right wall. Conclusion: Fetal tetralogy of Fallot have characteristic ultrasound images, prenatal color Doppler ultrasonographic can diagnoses fetal tetralogy of Fallot correctly and has important clinical value. (authors)

Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

Science.gov (United States)

Ansari, Amir H; Cherian, Perumpillichira J; Caicedo, Alexander; Naulaers, Gunnar; De Vos, Maarten; Van Huffel, Sabine

2018-04-02

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

Ultrasonographic findings of retinoblastoma

International Nuclear Information System (INIS)

Chung, Sung Hoo; Kang, Ik Won; Park, Yang Hee; Kim, Chu Wan; Chi, Je Geun

1982-01-01

Retinoblastoma is the most common intraocular tumor in infants and young children which has relatively favorable prognosis with early diagnosis and adequate treatment, however, it can be lethal if the treatment is delayed or inadequate. Clinically, early diagnosis is often difficult because of minimal subjective and objective signs and symptoms, and the patients are usually too young to complain visual disturbance. When ophthalmoscopicexamination is impossible due to presence of opaue media in front of tumor mass as associated inflammatory reaction, hemorrhage, corneal opacity, retinal detachment, etc, ultrasonography is necessary for diagnosis of retinoblastoma. Authors analyzed ultrasonographic al findings with pathological correlation on 10 cases of confirmed retinoblastoma during the period of March 1981 to September1982 at the Seoul National University Hospital. In all cases, ultrasonography demonstrates intraocular masses and all of which are cystic type.Reflectivity of masses are higher than retroorbital fat tissue in 8 cases, and 7 cases show irregular internal echogenic texture. There is no correlation between reflexivity and internal echogenic texture with microscopic findings as rosette, pseudo rosette and micro cysts. Calcifications are demonstrated by ultrasonography as strong reflectiveness with posterior sonic shadowing in 9 cases and 9 of 10 cases are well correlated with calcifications in pathologic specimens. Anechoic cystic areas are shown in 9 cases, and 6 of 10 cases are well correlated with necrosis in pathologic specimen. In all cases, there is no attenuation of sound within tumor masses, and no demonstrable choroidal excavation. Associated retinal detachment is hardly identifiable in irregular contour and internal texture of cystic tumor masses

Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

Science.gov (United States)

Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat

2017-09-27

An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

Serologic and ultrasonographic parameters of praziquantel treatment of hepatic fibrosis in Schistosoma japonicum infection.

Science.gov (United States)

Ohmae, H; Tanaka, M; Nara, T; Utsunomiya, H; Taguchi, H; Irie, Y; Yasuraoka, K

1991-09-01

We describe the parameters useful in evaluating the development of hepatic fibrosis in Schistosoma japonicum infection, as well as its improvement after treatment with praziquantel (PZQ). Various serologic parameters and ultrasonographic images were examined, and their changes were monitored using rabbits infected with 200 or 300 cercariae of S. japonicum. Infected rabbits were administered one oral treatment of PZQ at a dosage of 100 mg/kg at 6, 12, or 24 weeks after infection. Histopathologic examinations revealed that PZQ had a strong and rapid effect, even on damage that developed long after the infection. The improvement of moderate hepatic fibrosis that developed over 24 weeks after infection was also detected by histopathologic examinations. The serum level of total bile acid was the most sensitive parameter in evaluating the severity of hepatic fibrosis and its improvement after treatment with PZQ. The level of serum procollagen-III-peptide was also useful in evaluating the development of hepatic fibrosis, but not in its improvement. Ultrasonography revealed specific echogenic bands and nodules according to the progress of granuloma formation and fibrosis, and the reversal of these changes could also be observed after treatment with PZQ.

Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement

Science.gov (United States)

Negri, Lucas; Nied, Ademir; Kalinowski, Hypolito; Paterno, Aleksander

2011-01-01

This paper presents a benchmark for peak detection algorithms employed in fiber Bragg grating spectrometric interrogation systems. The accuracy, precision, and computational performance of currently used algorithms and those of a new proposed artificial neural network algorithm are compared. Centroid and gaussian fitting algorithms are shown to have the highest precision but produce systematic errors that depend on the FBG refractive index modulation profile. The proposed neural network displays relatively good precision with reduced systematic errors and improved computational performance when compared to other networks. Additionally, suitable algorithms may be chosen with the general guidelines presented. PMID:22163806

Towards a magnetoresistive platform for neural signal recording

Science.gov (United States)

Sharma, P. P.; Gervasoni, G.; Albisetti, E.; D'Ercoli, F.; Monticelli, M.; Moretti, D.; Forte, N.; Rocchi, A.; Ferrari, G.; Baldelli, P.; Sampietro, M.; Benfenati, F.; Bertacco, R.; Petti, D.

2017-05-01

A promising strategy to get deeper insight on brain functionalities relies on the investigation of neural activities at the cellular and sub-cellular level. In this framework, methods for recording neuron electrical activity have gained interest over the years. Main technological challenges are associated to finding highly sensitive detection schemes, providing considerable spatial and temporal resolution. Moreover, the possibility to perform non-invasive assays would constitute a noteworthy benefit. In this work, we present a magnetoresistive platform for the detection of the action potential propagation in neural cells. Such platform allows, in perspective, the in vitro recording of neural signals arising from single neurons, neural networks and brain slices.

Detection of outliers by neural network on the gas centrifuge experimental data of isotopic separation process

International Nuclear Information System (INIS)

Andrade, Monica de Carvalho Vasconcelos

2004-01-01

This work presents and discusses the neural network technique aiming at the detection of outliers on a set of gas centrifuge isotope separation experimental data. In order to evaluate the application of this new technique, the result obtained of the detection is compared to the result of the statistical analysis combined with the cluster analysis. This method for the detection of outliers presents a considerable potential in the field of data analysis and it is at the same time easier and faster to use and requests very less knowledge of the physics involved in the process. This work established a procedure for detecting experiments which are suspect to contain gross errors inside a data set where the usual techniques for identification of these errors cannot be applied or its use/demands an excessively long work. (author)

Model-based fault detection and isolation of a PWR nuclear power plant using neural networks

International Nuclear Information System (INIS)

Far, R.R.; Davilu, H.; Lucas, C.

2008-01-01

The proper and timely fault detection and isolation of industrial plant is of premier importance to guarantee the safe and reliable operation of industrial plants. The paper presents application of a neural networks-based scheme for fault detection and isolation, for the pressurizer of a PWR nuclear power plant. The scheme is constituted by 2 components: residual generation and fault isolation. The first component generates residuals via the discrepancy between measurements coming from the plant and a nominal model. The neutral network estimator is trained with healthy data collected from a full-scale simulator. For the second component detection thresholds are used to encode the residuals as bipolar vectors which represent fault patterns. These patterns are stored in an associative memory based on a recurrent neutral network. The proposed fault diagnosis tool is evaluated on-line via a full-scale simulator detected and isolate the main faults appearing in the pressurizer of a PWR. (orig.)

Cephalometric landmark detection in dental x-ray images using convolutional neural networks

Science.gov (United States)

Lee, Hansang; Park, Minseok; Kim, Junmo

2017-03-01

In dental X-ray images, an accurate detection of cephalometric landmarks plays an important role in clinical diagnosis, treatment and surgical decisions for dental problems. In this work, we propose an end-to-end deep learning system for cephalometric landmark detection in dental X-ray images, using convolutional neural networks (CNN). For detecting 19 cephalometric landmarks in dental X-ray images, we develop a detection system using CNN-based coordinate-wise regression systems. By viewing x- and y-coordinates of all landmarks as 38 independent variables, multiple CNN-based regression systems are constructed to predict the coordinate variables from input X-ray images. First, each coordinate variable is normalized by the length of either height or width of an image. For each normalized coordinate variable, a CNN-based regression system is trained on training images and corresponding coordinate variable, which is a variable to be regressed. We train 38 regression systems with the same CNN structure on coordinate variables, respectively. Finally, we compute 38 coordinate variables with these trained systems from unseen images and extract 19 landmarks by pairing the regressed coordinates. In experiments, the public database from the Grand Challenges in Dental X-ray Image Analysis in ISBI 2015 was used and the proposed system showed promising performance by successfully locating the cephalometric landmarks within considerable margins from the ground truths.

A fully automatic microcalcification detection approach based on deep convolution neural network

Science.gov (United States)

Cai, Guanxiong; Guo, Yanhui; Zhang, Yaqin; Qin, Genggeng; Zhou, Yuanpin; Lu, Yao

2018-02-01

Breast cancer is one of the most common cancers and has high morbidity and mortality worldwide, posing a serious threat to the health of human beings. The emergence of microcalcifications (MCs) is an important signal of early breast cancer. However, it is still challenging and time consuming for radiologists to identify some tiny and subtle individual MCs in mammograms. This study proposed a novel computer-aided MC detection algorithm on the full field digital mammograms (FFDMs) using deep convolution neural network (DCNN). Firstly, a MC candidate detection system was used to obtain potential MC candidates. Then a DCNN was trained using a novel adaptive learning strategy, neutrosophic reinforcement sample learning (NRSL) strategy to speed up the learning process. The trained DCNN served to recognize true MCs. After been classified by DCNN, a density-based regional clustering method was imposed to form MC clusters. The accuracy of the DCNN with our proposed NRSL strategy converges faster and goes higher than the traditional DCNN at same epochs, and the obtained an accuracy of 99.87% on training set, 95.12% on validation set, and 93.68% on testing set at epoch 40. For cluster-based MC cluster detection evaluation, a sensitivity of 90% was achieved at 0.13 false positives (FPs) per image. The obtained results demonstrate that the designed DCNN plays a significant role in the MC detection after being prior trained.

Damage detection in carbon composite material typical of wind turbine blades using auto-associative neural networks

Science.gov (United States)

Dervilis, N.; Barthorpe, R. J.; Antoniadou, I.; Staszewski, W. J.; Worden, K.

2012-04-01

The structure of a wind turbine blade plays a vital role in the mechanical and structural operation of the turbine. As new generations of offshore wind turbines are trying to achieve a leading role in the energy market, key challenges such as a reliable Structural Health Monitoring (SHM) of the blades is significant for the economic and structural efficiency of the wind energy. Fault diagnosis of wind turbine blades is a "grand challenge" due to their composite nature, weight and length. The damage detection procedure involves additional difficulties focused on aerodynamic loads, environmental conditions and gravitational loads. It will be shown that vibration dynamic response data combined with AANNs is a robust and powerful tool, offering on-line and real time damage prediction. In this study the features used for SHM are Frequency Response Functions (FRFs) acquired via experimental methods based on an LMS system by which identification of mode shapes and natural frequencies is accomplished. The methods used are statistical outlier analysis which allows a diagnosis of deviation from normality and an Auto-Associative Neural Network (AANN). Both of these techniques are trained by adopting the FRF data for normal and damage condition. The AANN is a method which has not yet been widely used in the condition monitoring of composite materials of blades. This paper is trying to introduce a new scheme for damage detection, localisation and severity assessment by adopting simple measurements such as FRFs and exploiting multilayer neural networks and outlier novelty detection.

Color Doppler Ultrasonographic Features of Hashimoto's Thyroiditis

Energy Technology Data Exchange (ETDEWEB)

Lee, Joo Hyuk; Kim, Mie Young; Rho, Eun Jin; Yi, Jeong Geun; Han, Chun Hwan [Kangnam General Hospital Public Corporation, Seoul (Korea, Republic of); Hwang, Hee Yong [Choong Ang Gil Hospital, Incheon (Korea, Republic of)

1995-06-15

Color Doppler ultrasonographic(US) features of 28 patients with Hashimato's thyroiditis were evaluated with regard to echo and color-flow patterns. Correlation of color-flow pattern with thyroid function was performed. All 28 patients showed varying degrees of diffuse enlargement of the thyroid gland and a heterogeneous echo pattern.Color-flow pattern of increased blood flow. Low to moderate, focally increased blood flow was seen in 26 patients(92.8%). Of these 26 patients, 24 patients showed subclinical hypothyroidism or euthyroidism. Two patients who showed hyperthyroidism showed several pieces of focally increased color flow, Which was noted during both systole and diastole. Diffuse, multifocal color-flow throughout thyroid gland was seen in two patients with Hashimato's thyroiditis: one with clinical hypothyroidism and the other with subclinical hypothyroidism. Even though Hashimoto's thyroiditis showed variable color-flow patterns, we believe that heterogenous parenchymal echopattern with low or moderately increased flow is a rather characteristic feature of Hashimoto's thyroiditis, and we suggest that color Doppler US provides additional information for evaluation of Hashimoto's thyroiditis

Color Doppler Ultrasonographic Features of Hashimoto's Thyroiditis

International Nuclear Information System (INIS)

Lee, Joo Hyuk; Kim, Mie Young; Rho, Eun Jin; Yi, Jeong Geun; Han, Chun Hwan; Hwang, Hee Yong

1995-01-01

Color Doppler ultrasonographic(US) features of 28 patients with Hashimato's thyroiditis were evaluated with regard to echo and color-flow patterns. Correlation of color-flow pattern with thyroid function was performed. All 28 patients showed varying degrees of diffuse enlargement of the thyroid gland and a heterogeneous echo pattern.Color-flow pattern of increased blood flow. Low to moderate, focally increased blood flow was seen in 26 patients(92.8%). Of these 26 patients, 24 patients showed subclinical hypothyroidism or euthyroidism. Two patients who showed hyperthyroidism showed several pieces of focally increased color flow, Which was noted during both systole and diastole. Diffuse, multifocal color-flow throughout thyroid gland was seen in two patients with Hashimato's thyroiditis: one with clinical hypothyroidism and the other with subclinical hypothyroidism. Even though Hashimoto's thyroiditis showed variable color-flow patterns, we believe that heterogenous parenchymal echopattern with low or moderately increased flow is a rather characteristic feature of Hashimoto's thyroiditis, and we suggest that color Doppler US provides additional information for evaluation of Hashimoto's thyroiditis

A light and faster regional convolutional neural network for object detection in optical remote sensing images

Science.gov (United States)

Ding, Peng; Zhang, Ye; Deng, Wei-Jian; Jia, Ping; Kuijper, Arjan

2018-07-01

Detection of objects from satellite optical remote sensing images is very important for many commercial and governmental applications. With the development of deep convolutional neural networks (deep CNNs), the field of object detection has seen tremendous advances. Currently, objects in satellite remote sensing images can be detected using deep CNNs. In general, optical remote sensing images contain many dense and small objects, and the use of the original Faster Regional CNN framework does not yield a suitably high precision. Therefore, after careful analysis we adopt dense convoluted networks, a multi-scale representation and various combinations of improvement schemes to enhance the structure of the base VGG16-Net for improving the precision. We propose an approach to reduce the test-time (detection time) and memory requirements. To validate the effectiveness of our approach, we perform experiments using satellite remote sensing image datasets of aircraft and automobiles. The results show that the improved network structure can detect objects in satellite optical remote sensing images more accurately and efficiently.

Ultrasonographic reproductive tract measures and pelvis measures as predictors of pregnancy failure and anestrus in restricted bred beef heifers

NARCIS (Netherlands)

Holm, Dietmar E; Nielen, Mirjam; Jorritsma, Ruurd; Irons, Peter C; Thompson, Peter N

Previous reports have shown that reproductive tract score (RTS) can predict reproduction outcomes in seasonally bred beef heifers, although the accuracy can vary. Some ultrasonographic measures of the female reproductive tract and pelvis area have also been associated with reproductive outcome in

Aerial Images and Convolutional Neural Network for Cotton Bloom Detection.

Science.gov (United States)

Xu, Rui; Li, Changying; Paterson, Andrew H; Jiang, Yu; Sun, Shangpeng; Robertson, Jon S

2017-01-01

Monitoring flower development can provide useful information for production management, estimating yield and selecting specific genotypes of crops. The main goal of this study was to develop a methodology to detect and count cotton flowers, or blooms, using color images acquired by an unmanned aerial system. The aerial images were collected from two test fields in 4 days. A convolutional neural network (CNN) was designed and trained to detect cotton blooms in raw images, and their 3D locations were calculated using the dense point cloud constructed from the aerial images with the structure from motion method. The quality of the dense point cloud was analyzed and plots with poor quality were excluded from data analysis. A constrained clustering algorithm was developed to register the same bloom detected from different images based on the 3D location of the bloom. The accuracy and incompleteness of the dense point cloud were analyzed because they affected the accuracy of the 3D location of the blooms and thus the accuracy of the bloom registration result. The constrained clustering algorithm was validated using simulated data, showing good efficiency and accuracy. The bloom count from the proposed method was comparable with the number counted manually with an error of -4 to 3 blooms for the field with a single plant per plot. However, more plots were underestimated in the field with multiple plants per plot due to hidden blooms that were not captured by the aerial images. The proposed methodology provides a high-throughput method to continuously monitor the flowering progress of cotton.

Effects of corn oil administered orally on conspicuity of ultrasonographic small intestinal lesions in dogs with lymphangiectasia.

Science.gov (United States)

Pollard, Rachel E; Johnson, Eric G; Pesavento, Patricia A; Baker, Tomas W; Cannon, Allison B; Kass, Philip H; Marks, Stanley L

2013-01-01

Lymphangiectasia is one of the causes of protein-losing enteropathy in dogs and characteristic ultrasonographic small intestinal lesions have been previously described. The purpose of this study was to determine whether corn oil administered orally (COAO) would result in increased conspicuity of these characteristic small intestinal ultrasonographic lesions in dogs with lymphangiectasia. Affected dogs were included if they underwent corn oil administered orally and had a surgical full-thickness intestinal biopsy diagnosis of lymphangiectasia. Control dogs had normal clinical examination and standard laboratory test findings. Ultrasound images of duodenum, jejunum, and ileum were obtained prior to and 30, 60, 90, and 120 min after corn oil administered orally for all dogs. Parameters recorded for each ultrasound study were intestinal wall thickness, mucosal echogenicity, and presence or absence of hyperechoic mucosal striations (HMS) and a parallel hyperechoic mucosal line (PHML). Nine affected and five controls dogs were included in the study. Seven of the nine dogs with lymphangiectasia had hyperechoic mucosal striations prior to corn oil administered orally. Jejunal hyperechoic mucosal striations were significantly associated with lymphangiectasia at multiple time points (P dogs with lymphangiectasia 60 or 90 min after corn oil administered orally. Increased mucosal echogenicity was observed in all dogs at multiple time points after corn oil administered orally. A parallel hyperechoic mucosal line was present in the jejunum in 4/5 healthy and 6/9 dogs with lymphangiectasia at one or more time points after corn oil administered orally. Findings indicated that corn oil administered orally improves conspicuity of characteristic ultrasonographic lesions in dogs with lymphangiectasia, however some of these lesions may also be present in healthy dogs that recently received a fatty meal. © 2013 Veterinary Radiology & Ultrasound.

Fibrocystic change in breast; mammographic and ultrasonographic findings in lower risk lesions

International Nuclear Information System (INIS)

Kook, Shin Ho; Jung, Kyung Jae; Noh, In Gye

1996-01-01

We performed this study to define the characteristic mammographic and ultrasonographic findings in lower risk lesions of fibrocystic change and also tried to evaluate the role of both modalities in planning the treatment of these lesions. We retrospectively reviewed 38 cases of mammography and 46 cases of ultrasonography in biopsy proven 55 cases of fibrocystic change, histologically showing the nonproliferative pattern or proliferative pattern without atypia. We analyzed the mammographic and ultrasonographic findings, final assessments, and compared the effectiveness of each modality. On mammography, there were no abnormatlities in 20 cases(53%), nodules or masses in 9 cases(24%), microcalcifications in 6 cases(16%) and asymmetric density in 5 cases(14%). On ultrasonography, there were 40 cases(87%) of focal sonographic abnormality and no abnormality in 6 cases(13%). Most focal sonographic abnormalities were smooth(40 cases, 93%), well-defined(21 cases, 49%) or ill-defined(22 cases, 51%) round or oval(36 cases, 84%) shaped, homogeneous(31 cases, 67%), hypoechoic(30 cases, 65%) lesions. Final assessment revealed that only 7 cases(18%) of mammography and 8 cases(18%) of ultrasound examinations were included into the category of indeterminate and malignancy groups which were recommended biopsy. Mammography was excellent to demonstrate the microcalcifications and ultrasonography was effective in depiction of the focal lesions. The mammography and ultrasonography findings were not specific in diagnosing lower risk group of fibrocystic change. But complementary study of both modalities in conjunction with clinical findings will be helpful in making decision among biopsy, fine needle aspiration, and simple close follow up of the lesions

Fibrocystic change in breast; mammographic and ultrasonographic findings in lower risk lesions

Energy Technology Data Exchange (ETDEWEB)

Kook, Shin Ho; Jung, Kyung Jae; Noh, In Gye [Kangbuk Samsung Hospital, Seoul (Korea, Republic of)

1996-01-01

We performed this study to define the characteristic mammographic and ultrasonographic findings in lower risk lesions of fibrocystic change and also tried to evaluate the role of both modalities in planning the treatment of these lesions. We retrospectively reviewed 38 cases of mammography and 46 cases of ultrasonography in biopsy proven 55 cases of fibrocystic change, histologically showing the nonproliferative pattern or proliferative pattern without atypia. We analyzed the mammographic and ultrasonographic findings, final assessments, and compared the effectiveness of each modality. On mammography, there were no abnormatlities in 20 cases(53%), nodules or masses in 9 cases(24%), microcalcifications in 6 cases(16%) and asymmetric density in 5 cases(14%). On ultrasonography, there were 40 cases(87%) of focal sonographic abnormality and no abnormality in 6 cases(13%). Most focal sonographic abnormalities were smooth(40 cases, 93%), well-defined(21 cases, 49%) or ill-defined(22 cases, 51%) round or oval(36 cases, 84%) shaped, homogeneous(31 cases, 67%), hypoechoic(30 cases, 65%) lesions. Final assessment revealed that only 7 cases(18%) of mammography and 8 cases(18%) of ultrasound examinations were included into the category of indeterminate and malignancy groups which were recommended biopsy. Mammography was excellent to demonstrate the microcalcifications and ultrasonography was effective in depiction of the focal lesions. The mammography and ultrasonography findings were not specific in diagnosing lower risk group of fibrocystic change. But complementary study of both modalities in conjunction with clinical findings will be helpful in making decision among biopsy, fine needle aspiration, and simple close follow up of the lesions.

Detection of broken rotor bar faults in induction motor at low load using neural network.

Science.gov (United States)

Bessam, B; Menacer, A; Boumehraz, M; Cherif, H

2016-09-01

The knowledge of the broken rotor bars characteristic frequencies and amplitudes has a great importance for all related diagnostic methods. The monitoring of motor faults requires a high resolution spectrum to separate different frequency components. The Discrete Fourier Transform (DFT) has been widely used to achieve these requirements. However, at low slip this technique cannot give good results. As a solution for these problems, this paper proposes an efficient technique based on a neural network approach and Hilbert transform (HT) for broken rotor bar diagnosis in induction machines at low load. The Hilbert transform is used to extract the stator current envelope (SCE). Two features are selected from the (SCE) spectrum (the amplitude and frequency of the harmonic). These features will be used as input for neural network. The results obtained are astonishing and it is capable to detect the correct number of broken rotor bars under different load conditions. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Serial ultrasonographic appearance of postpartum uterine involution in beagle dogs.

Science.gov (United States)

Yeager, A E; Concannon, P W

1990-09-01

Postpartum changes in uterine shape, architecture, echogenicity and diameter were determined during the serial examinations of five beagle bitches. During postpartum Week 1, the uterine horns were tubular structures composed of multiple layers of various echogenicity and had multiple, discrete enlargements with hypoechoic centers at placental sites. Diameters ranged from 1.1 to 3.8 cm at placental site enlargements, and 0.5 to 1.4 cm between enlargements. Uterine involution appeared to be completed by 15 wk post partum. At 15 weeks the uterine horns of each dog were uniform hypoechoic, tubular structures without enlargements and had a reduced diameter of 0.3 to 0.6 cm. These ultrasonographic findings are similar to previously reported gross and light microscopic descriptions of canine uterine involution.

Value of contrast-enhanced sonographic micro flow imaging for prostate cancer detection with t-PSA level of 4–10 ng/mL

International Nuclear Information System (INIS)

Guo, Yi-Fen; Li, Feng-Hua; Xie, Shao-Wei; Xia, Jian-Guo; Fang, Hua; Li, Hong-Li

2012-01-01

Objectives: To compare the efficiency of contrast-enhanced ultrasonographic micro flow imaging (MFI) with conventional transrectal ultrasound (TRUS) in detecting prostate cancer with serum total prostate-specific antigen (t-PSA) of 4.0–10.0 ng/mL. To evaluate the value of contrast-enhanced ultrasonographic MFI in detecting prostate cancer with t-PSA in diagnostic gray zone. Methods: 47 patients with t-PSA 4.0–10.0 ng/mL underwent gray scale, power Doppler TRUS and MFI examinations before ultrasound guided biopsies. Biopsies were performed at twelve sites in the base, the mid-gland and the apex of the prostate in each patient, when there was no abnormal ultrasound finding. When an abnormality was present at MFI, the biopsy specimen from the corresponding site was directed toward the abnormal finding. With histological results of prostate biopsy as reference standards, we assessed the cancer detection of these three methods. Results: 564 specimens were collected in this study, in which 101 were prostate cancer confirmed histologically. 152 of 564 specimens were demonstrated abnormal on MFI images, in which 71 were malignant and 81 were benign confirmed histologically. The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) for MFI in detecting prostate caner were 70.3%, 82.5%, 80.3%, 46.7% and 92.7%, respectively. The sensitivity and NPV for MFI were significantly better than gray scale (38.6%, 86.9%) and power Doppler (32.7%, 86.0%) (P < 0.001) TRUS. Conclusions: Contrast-enhanced ultrasonographic MFI could significantly improve the detection rate of prostate cancer with t-PSA in diagnostic gray zone (4–10 ng/mL) than conventional ultrasound.

Diagnostic sensitivity and interobserver agreement of radiography and ultrasonography for detecting trochlear ridge osteochondrosis lesions in the equine stifle.

Science.gov (United States)

Beccati, Francesca; Chalmers, Heather J; Dante, Sara; Lotto, Eleonora; Pepe, Marco

2013-01-01

Osteochondrosis lesions commonly occur on the femoral trochlear ridges in horses and radiography and ultrasonography are routinely used to diagnose these lesions. However, poor correlation has been found between radiographic and arthroscopic findings of affected trochlear ridges. Interobserver agreement for ultrasonographic diagnoses and correlation between ultrasonographic and arthroscopic findings have not been previously described. Objectives of this study were to describe diagnostic sensitivity and interobserver agreement of radiography and ultrasonography for detecting and grading osteochondrosis lesions of the equine trochlear ridges, using arthroscopy as the reference standard. Twenty-two horses were sampled. Two observers independently recorded radiographic and ultrasonographic findings without knowledge of arthroscopic findings. Imaging findings were compared between observers and with arthroscopic findings. Agreement between observers was moderate to excellent (κ 0.48-0.86) for detecting lesions using radiography and good to excellent (κ 0.74-0.87) for grading lesions using radiography. Agreement between observers was good to excellent (κ 0.78-0.94) for detecting lesions using ultrasonography and very good to excellent (κ 0.86-0.93) for grading lesions using ultrasonography. Diagnostic sensitivity was 84-88% for radiography and 100% for ultrasonography. Diagnostic specificity was 89-100% for radiography and 60-82% for ultrasonography. Agreement between radiography and arthroscopy was good (κ 0.64-0.78). Agreement between ultrasonography and arthroscopy was very good to excellent (κ 0.81-0.87). Findings from this study support ultrasound as a preferred method for predicting presence and severity of osteochondrosis lesions involving the femoral trochlear ridges in horses. © 2012 Veterinary Radiology & Ultrasound.

Usage of Probabilistic and General Regression Neural Network for Early Detection and Prevention of Oral Cancer

Directory of Open Access Journals (Sweden)

Neha Sharma

2015-01-01

Full Text Available In India, the oral cancers are usually presented in advanced stage of malignancy. It is critical to ascertain the diagnosis in order to initiate most advantageous treatment of the suspicious lesions. The main hurdle in appropriate treatment and control of oral cancer is identification and risk assessment of early disease in the community in a cost-effective fashion. The objective of this research is to design a data mining model using probabilistic neural network and general regression neural network (PNN/GRNN for early detection and prevention of oral malignancy. The model is built using the oral cancer database which has 35 attributes and 1025 records. All the attributes pertaining to clinical symptoms and history are considered to classify malignant and non-malignant cases. Subsequently, the model attempts to predict particular type of cancer, its stage and extent with the help of attributes pertaining to symptoms, gross examination and investigations. Also, the model envisages anticipating the survivability of a patient on the basis of treatment and follow-up details. Finally, the performance of the PNN/GRNN model is compared with that of other classification models. The classification accuracy of PNN/GRNN model is 80% and hence is better for early detection and prevention of the oral cancer.

Usage of Probabilistic and General Regression Neural Network for Early Detection and Prevention of Oral Cancer.

Science.gov (United States)

Sharma, Neha; Om, Hari

2015-01-01

In India, the oral cancers are usually presented in advanced stage of malignancy. It is critical to ascertain the diagnosis in order to initiate most advantageous treatment of the suspicious lesions. The main hurdle in appropriate treatment and control of oral cancer is identification and risk assessment of early disease in the community in a cost-effective fashion. The objective of this research is to design a data mining model using probabilistic neural network and general regression neural network (PNN/GRNN) for early detection and prevention of oral malignancy. The model is built using the oral cancer database which has 35 attributes and 1025 records. All the attributes pertaining to clinical symptoms and history are considered to classify malignant and non-malignant cases. Subsequently, the model attempts to predict particular type of cancer, its stage and extent with the help of attributes pertaining to symptoms, gross examination and investigations. Also, the model envisages anticipating the survivability of a patient on the basis of treatment and follow-up details. Finally, the performance of the PNN/GRNN model is compared with that of other classification models. The classification accuracy of PNN/GRNN model is 80% and hence is better for early detection and prevention of the oral cancer.

Ultrasonographically documented early pregnancy loss in an Asian elephant (Elephas maximus).

Science.gov (United States)

Lueders, Imke; Drews, Barbara; Niemuller, Cheryl; Gray, Charlie; Rich, Peter; Fickel, Jörns; Wibbelt, Gudrun; Göritz, Frank; Hildebrandt, Thomas B

2010-01-01

Early embryonic resorption or fetal loss is known to occur occasionally in captive elephants; however, this has mostly been reported anecdotally. The present study documents the case of a 24-year-old, multiparous Asian elephant cow that suffered embryonic death and resorption at around 18 weeks of gestation. From ovulation onwards, this female was sonographically examined 58 times. Blood was collected twice weekly for progestagen determination via enzyme immunoassay. On Day 42 after ovulation, a small quantity of fluid was detected in the uterine horn, which typically indicates the presence of a developing conceptus. Repeated inspections followed what appeared to be a normal pregnancy until Day 116. However, on Day 124, signs of embryonic life were absent. Progestagen concentrations started declining two weeks later, reaching baseline levels one month after embryonic death. Retrospectively, ultrasound examination revealed several abnormalities in the uterine horn. Besides an existing leiomyoma, multiple small cystic structures had formed in the endometrium at the implantation site and later in the placenta. These pathological findings were considered as possible contributors to the early pregnancy failure. PCR for endotheliotropic elephant herpes virus (EEHV) (which had occurred previously in the herd) as well as serology for other infectious organisms known to cause abortion in domestic animals did not yield any positive results. Although no definitive reason was found for this pregnancy to abort, this ultrasonographically and endocrinologically documented study of an early pregnancy loss provides important insights into the resorption process in Asian elephants.

The neural signature of emotional memories in serial crimes.

Science.gov (United States)

Chassy, Philippe

2017-10-01

Neural plasticity is the process whereby semantic information and emotional responses are stored in neural networks. It is hypothesized that the neural networks built over time to encode the sexual fantasies that motivate serial killers to act should display a unique, detectable activation pattern. The pathological neural watermark hypothesis posits that such networks comprise activation of brain sites that reflect four cognitive components: autobiographical memory, sexual arousal, aggression, and control over aggression. The neural sites performing these cognitive functions have been successfully identified by previous research. The key findings are reviewed to hypothesise the typical pattern of activity that serial killers should display. Through the integration of biological findings into one framework, the neural approach proposed in this paper is in stark contrast with the many theories accounting for serial killers that offer non-medical taxonomies. The pathological neural watermark hypothesis offers a new framework to understand and detect deviant individuals. The technical and legal issues are briefly discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Ultrasonographic anatomy of the healthy southern tigrina ( Leopardus guttulus) abdomen: comparison with domestic cat references.

Science.gov (United States)

Müller, Thiago R; Marcelino, Raquel S; de Souza, Livia P; Teixeira, Carlos R; Mamprim, Maria J

2017-02-01

Objectives The aim of the study was to describe the normal abdominal echoanatomy of the tigrina and to compare it with the abdominal echoanatomy of the domestic cat. Reference intervals for the normal abdominal ultrasonographic anatomy of individual species are important for accurate diagnoses and interpretation of routine health examinations. The hypothesis was that the echoanatomy of the tigrina was similar to that of the domestic cat. Methods Eighteen clinically healthy tigrina were selected for abdominal ultrasound examination, in order to obtain normal parameters of the bladder, spleen, adrenal gland, kidney, gastrointestinal tract, liver and gall bladder, and Doppler parameters of liver and kidney vessels. Results The splenic parenchyma was consistently hyperechoic to the kidneys and liver. The liver, kidneys and spleen had similar echotexture, shape and dimensions when compared with the domestic cat. The gall bladder was lobulated and surrounded by a clearly visualized thin, smooth, regular echogenic wall. The adrenal glands had a bilobulated shape. The urinary bladder had a thin echogenic wall. The Doppler parameters of the portal vein and renal artery were similar to the domestic cat. Conclusions and relevance The results support the hypothesis that the ultrasonographic parameters of the abdominal viscera of the southern tigrina are similar to those of the domestic cat.

Two neural network based strategies for the detection of a total instantaneous blockage of a sodium-cooled fast reactor

International Nuclear Information System (INIS)

Martinez-Martinez, Sinuhe; Messai, Nadhir; Jeannot, Jean-Philippe; Nuzillard, Danielle

2015-01-01

The total instantaneous blockage (TIB) of an assembly in the core of a sodium-cooled fast reactor (SFR) is investigated. Such incident could appear as an abnormal rise in temperature on the assemblies neighbouring the blockage. Its detection relies on a dataset of temperature measurements of the assemblies making up the core of the French Phenix Nuclear Reactor. The data are provided by the French Commission of Atomic and Alternatives Energies (CEA). Here, two strategies are proposed depending on whether the sensor measurement of the suspected assembly is reliable or not. The proposed methodology implements a time-lagged feed-forward neural (TLFFN) Network in order to predict the one-step-ahead temperature of a given assembly. The incident is declared if the difference between the predicted process and the actual one exceeds a threshold. In these simulated conditions, the method is efficient to detect small gradients as expected in reality. - Highlights: • We study the total instantaneous blockage (TIB) of a sodium-cooled fast reactor. • The TIB symptom is simulated as an abrupt rise on temperature (0.1–1 °C/s). • The goal is to improve the early detection of the incident. • Two strategies laying on neural networks are proposed. • TIB is detected in 3 s for 1 °C/s and 18–21 s for 0.1 °C/s

Can surgical simulation be used to train detection and classification of neural networks?

Science.gov (United States)

Zisimopoulos, Odysseas; Flouty, Evangello; Stacey, Mark; Muscroft, Sam; Giataganas, Petros; Nehme, Jean; Chow, Andre; Stoyanov, Danail

2017-10-01

Computer-assisted interventions (CAI) aim to increase the effectiveness, precision and repeatability of procedures to improve surgical outcomes. The presence and motion of surgical tools is a key information input for CAI surgical phase recognition algorithms. Vision-based tool detection and recognition approaches are an attractive solution and can be designed to take advantage of the powerful deep learning paradigm that is rapidly advancing image recognition and classification. The challenge for such algorithms is the availability and quality of labelled data used for training. In this Letter, surgical simulation is used to train tool detection and segmentation based on deep convolutional neural networks and generative adversarial networks. The authors experiment with two network architectures for image segmentation in tool classes commonly encountered during cataract surgery. A commercially-available simulator is used to create a simulated cataract dataset for training models prior to performing transfer learning on real surgical data. To the best of authors' knowledge, this is the first attempt to train deep learning models for surgical instrument detection on simulated data while demonstrating promising results to generalise on real data. Results indicate that simulated data does have some potential for training advanced classification methods for CAI systems.

Detection of anti-streptococcal, antienolase, and anti-neural antibodies in subjects with early-onset psychiatric disorders.

Science.gov (United States)

Nicolini, Humberto; López, Yaumara; Genis-Mendoza, Alma D; Manrique, Viana; Lopez-Canovas, Lilia; Niubo, Esperanza; Hernández, Lázaro; Bobes, María A; Riverón, Ana M; López-Casamichana, Mavil; Flores, Julio; Lanzagorta, Nuria; De la Fuente-Sandoval, Camilo; Santana, Daniel

2015-01-01

Infection with group A Streptococcus (StrepA) can cause post-infectious sequelae, including a spectrum of childhood-onset obsessive-compulsive (OCD) and tic disorders with autoimmune origin (PANDAS, Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections). Until now, no single immunological test has been designed that unequivocally diagnoses these disorders. In this study, we assessed the detection of serum antibodies against human brain enolase (AE), neural tissue (AN) and Streptococcus (AS) as a laboratory tool for the diagnosis of early-onset psychiatric disorders. Serum antibodies against human brain enolase, total brain proteins, and total proteins from StrepA were detected by ELISA in 37 patients with a presumptive diagnosis of PANDAS and in 12 healthy subjects from Mexico and Cuba. The antibody titers against human brain enolase (AE) and Streptococcal proteins (AS) were higher in patients than in control subjects (t-student, tAE=-2.17, P=0.035; tAS=-2.68, P=0.01, n=12 and 37/group, df=47, significance level 0.05), while the neural antibody titers did not differ between the two groups (P(t)=0.05). The number of subjects (titers> meancontrol + CI95) with simultaneous seropositivity to all three antibodies was higher in the patient group (51.4%) than in the control group (8.3%) group (X2=5.27, P=0.022, df=1, n=49). The simultaneous detection of all three of these antibodies could provide valuable information for the etiologic diagnosis of individuals with early-onset obsessive-compulsive disorders associated with streptococcal infection and, consequently, for prescribing suitable therapy.

Traffic sign recognition with deep convolutional neural networks

OpenAIRE

Karamatić, Boris

2016-01-01

The problem of detection and recognition of traffic signs is becoming an important problem when it comes to the development of self driving cars and advanced driver assistance systems. In this thesis we will develop a system for detection and recognition of traffic signs. For the problem of detection we will use aggregate channel features and for the problem of recognition we will use a deep convolutional neural network. We will describe how convolutional neural networks work, how they are co...

Cerebral microbleed detection in traumatic brain injury patients using 3D convolutional neural networks

Science.gov (United States)

Standvoss, K.; Crijns, T.; Goerke, L.; Janssen, D.; Kern, S.; van Niedek, T.; van Vugt, J.; Alfonso Burgos, N.; Gerritse, E. J.; Mol, J.; van de Vooren, D.; Ghafoorian, M.; van den Heuvel, T. L. A.; Manniesing, R.

2018-02-01

The number and location of cerebral microbleeds (CMBs) in patients with traumatic brain injury (TBI) is important to determine the severity of trauma and may hold prognostic value for patient outcome. However, manual assessment is subjective and time-consuming due to the resemblance of CMBs to blood vessels, the possible presence of imaging artifacts, and the typical heterogeneity of trauma imaging data. In this work, we present a computer aided detection system based on 3D convolutional neural networks for detecting CMBs in 3D susceptibility weighted images. Network architectures with varying depth were evaluated. Data augmentation techniques were employed to improve the networks' generalization ability and selective sampling was implemented to handle class imbalance. The predictions of the models were clustered using a connected component analysis. The system was trained on ten annotated scans and evaluated on an independent test set of eight scans. Despite this limited data set, the system reached a sensitivity of 0.87 at 16.75 false positives per scan (2.5 false positives per CMB), outperforming related work on CMB detection in TBI patients.

Cascade Convolutional Neural Network Based on Transfer-Learning for Aircraft Detection on High-Resolution Remote Sensing Images

Directory of Open Access Journals (Sweden)

Bin Pan

2017-01-01

Full Text Available Aircraft detection from high-resolution remote sensing images is important for civil and military applications. Recently, detection methods based on deep learning have rapidly advanced. However, they require numerous samples to train the detection model and cannot be directly used to efficiently handle large-area remote sensing images. A weakly supervised learning method (WSLM can detect a target with few samples. However, it cannot extract an adequate number of features, and the detection accuracy requires improvement. We propose a cascade convolutional neural network (CCNN framework based on transfer-learning and geometric feature constraints (GFC for aircraft detection. It achieves high accuracy and efficient detection with relatively few samples. A high-accuracy detection model is first obtained using transfer-learning to fine-tune pretrained models with few samples. Then, a GFC region proposal filtering method improves detection efficiency. The CCNN framework completes the aircraft detection for large-area remote sensing images. The framework first-level network is an image classifier, which filters the entire image, excluding most areas with no aircraft. The second-level network is an object detector, which rapidly detects aircraft from the first-level network output. Compared with WSLM, detection accuracy increased by 3.66%, false detection decreased by 64%, and missed detection decreased by 23.1%.

Mini-review: the ultrasonographical and serological chanbges and their improvement after praziquantel treatment Schistosoma japonicum infected patients in Leyete, Philippines

Directory of Open Access Journals (Sweden)

Manami Tanaka

1992-01-01

Full Text Available We have identified the specific ultrasonographical (US changes in Schistosoma japonicum infected patients with the serological changes in general liver function markers. The US examination with the following haematological and biochemical serum analysis was performed on 102 patients in Shistosomiasis Hospital, Leyte, Philippines. The US liver images were classified into 4 patterns according to the development of periportal fibrosis and the patterns of echogenic bands. Among various haematological and biochemical serum parameters of liver damage. The serum levels of total bile acid (TBA and procollagen-III-peptide (P-III-P correlated well with the development of hepatic fibrosis and the portal hypertension. These patients were subsequently treated with praziquantel (3 x 20 mg/kg, and improvement of the thickening of the portal vein wall and the dintensity of the echogenic band formation was detected 6 months after treatment. The significant US changes could not be detected in the patients with severe hepatic fibrosis caused in the long term infection. The results revealed that the US examination with the serum TBa level would provider a sensitive tool monitor the severity of the infection and also the improvement occured shortly after praziquantel treatment.

Ultrasonographic assessment of tendon thickness, Doppler activity and bony spurs of the elbow in patients with lateral epicondylitis and healthy subjects

DEFF Research Database (Denmark)

Krogh, T P; Fredberg, U; Christensen, Robin

2013-01-01

PURPOSE: Tennis elbow, also known as lateral epicondylitis (LE), is a common disorder often assessed by ultrasound. The aim of this study was to evaluate the ultrasonographic outcomes and methods used in LE research and clinical practice. MATERIALS AND METHODS: This study was designed as an intra......- and interobserver reliability and agreement study. Ultrasonographic examination of the common extensor tendon of the elbow was performed. The intraobserver study examined tendon thickness twice in 20 right elbows from 20 healthy individuals at an interval of 7 to 12 days. The interobserver study examined tendon...... thickness, color Doppler activity, and bony spurs in 18 right elbows in 9 healthy individuals and 9 patients with LE. Two trained rheumatologists performed the interobserver examinations with the same scanner on the same day. The main outcomes were intra- and interclass correlation (ICC) and agreement...

Ultrasonographic anatomy of the coelomic organs of boid snakes (Boa constrictor imperator, Python regius, Python molurus molurus, and Python curtus).

Science.gov (United States)

Banzato, Tommaso; Russo, Elisa; Finotti, Luca; Milan, Maria C; Gianesella, Matteo; Zotti, Alessandro

2012-05-01

To determine the ultrasonographic features of the coelomic organs of healthy snakes belonging to the Boidae and Pythonidae families. 16 ball pythons (Python regius; 7 males, 8 females, and 1 sexually immature), 10 Indian rock pythons (Python molurus molurus; 5 males, 4 females, and 1 sexually immature), 12 Python curtus (5 males and 7 females), and 8 boa constrictors (Boa constrictor imperator; 4 males and 4 females). All snakes underwent complete ultrasonographic evaluation of the coelomic cavity; chemical restraint was not necessary. A dorsolateral approach to probe placement was chosen to increase image quality and to avoid injury to the snakes and operators. Qualitative and quantitative observations were recorded. The liver, stomach, gallbladder, pancreas, small and large intestines, kidneys, cloaca, and scent glands were identified in all snakes. The hemipenes were identified in 10 of the 21 (48%) male snakes. The spleen was identified in 5 of the 46 (11%) snakes, and ureters were identified in 6 (13%). In 2 sexually immature snakes, the gonads were not visible. One (2%) snake was gravid, and 7 (15%) had small amounts of free fluid in the coelomic cavity. A significant positive correlation was identified between several measurements (diameter and thickness of scent glands, gastric and pyloric walls, and colonic wall) and body length (snout to vent) and body weight. The study findings can be used as an atlas of the ultrasonographic anatomy of the coelomic cavity in healthy boid snakes. Ultrasonography was reasonably fast to perform and was well tolerated in conscious snakes.

Individual Identification Using Functional Brain Fingerprint Detected by Recurrent Neural Network.

Science.gov (United States)

Chen, Shiyang; Hu, Xiaoping P

2018-03-20

Individual identification based on brain function has gained traction in literature. Investigating individual differences in brain function can provide additional insights into the brain. In this work, we introduce a recurrent neural network based model for identifying individuals based on only a short segment of resting state functional MRI data. In addition, we demonstrate how the global signal and differences in atlases affect the individual identifiability. Furthermore, we investigate neural network features that exhibit the uniqueness of each individual. The results indicate that our model is able to identify individuals based on neural features and provides additional information regarding brain dynamics.

The value of ultrasonographic examinations in the diagnosis of focal changes in the hepatic parenchyma; Wartosc badania ultrasonograficznego w rozpoznawaniu zmian ogniskowych w miazszu watroby u psow i kotow

Energy Technology Data Exchange (ETDEWEB)

Narojek, T. [Szkola Glowna Gospodarstwa Wiejskiego, Warsaw (Poland)

1995-12-31

The aim of the investigation was the comparison of the diagnostic value of the clinical, radiological and ultrasonographic examinations in the diagnosis of focal changes in the liver and the determination of the relations between the changes in the ultrasound image of the liver and the changes in other organs. The investigation was performed on 24 animals: 20 dogs and 4 cats of different breeds and sex, aged 1.5 to 14 years. The ultrasonographic examinations were done using the apparatus of Bruel and Kjaer type 1849 and Concept 2000 of Dynamic Imaging. The following changes were diagnosed in the ultrasonic picture: echogenic changes in 5 animals, hypoechogenic in 2 animals, normechogenic in 2 animals, hyperechogenic in 8 animals and changes of mixed echogenicity in 5 animals. The connection between clinical signs and the results of X-ray and ultrasonographic examination allowed the recognition of the changes in the liver as cysts, abscess and neoplasm of the liver. (author). 9 refs, 8 figs, 5 tabs.

Local community detection as pattern restoration by attractor dynamics of recurrent neural networks.

Science.gov (United States)

Okamoto, Hiroshi

2016-08-01

Densely connected parts in networks are referred to as "communities". Community structure is a hallmark of a variety of real-world networks. Individual communities in networks form functional modules of complex systems described by networks. Therefore, finding communities in networks is essential to approaching and understanding complex systems described by networks. In fact, network science has made a great deal of effort to develop effective and efficient methods for detecting communities in networks. Here we put forward a type of community detection, which has been little examined so far but will be practically useful. Suppose that we are given a set of source nodes that includes some (but not all) of "true" members of a particular community; suppose also that the set includes some nodes that are not the members of this community (i.e., "false" members of the community). We propose to detect the community from this "imperfect" and "inaccurate" set of source nodes using attractor dynamics of recurrent neural networks. Community detection by the proposed method can be viewed as restoration of the original pattern from a deteriorated pattern, which is analogous to cue-triggered recall of short-term memory in the brain. We demonstrate the effectiveness of the proposed method using synthetic networks and real social networks for which correct communities are known. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Fluid pipeline system leak detection based on neural network and pattern recognition

International Nuclear Information System (INIS)

Tang Xiujia

1998-01-01

The mechanism of the stress wave propagation along the pipeline system of NPP, caused by turbulent ejection from pipeline leakage, is researched. A series of characteristic index are described in time domain or frequency domain, and compress numerical algorithm is developed for original data compression. A back propagation neural networks (BPNN) with the input matrix composed by stress wave characteristics in time domain or frequency domain is first proposed to classify various situations of the pipeline, in order to detect the leakage in the fluid flow pipelines. The capability of the new method had been demonstrated by experiments and finally used to design a handy instrument for the pipeline leakage detection. Usually a pipeline system has many inner branches and often in adjusting dynamic condition, it is difficult for traditional pipeline diagnosis facilities to identify the difference between inner pipeline operation and pipeline fault. The author first proposed pipeline wave propagation identification by pattern recognition to diagnose pipeline leak. A series of pattern primitives such as peaks, valleys, horizon lines, capstan peaks, dominant relations, slave relations, etc., are used to extract features of the negative pressure wave form. The context-free grammar of symbolic representation of the negative wave form is used, and a negative wave form parsing system with application to structural pattern recognition based on the representation is first proposed to detect and localize leaks of the fluid pipelines

Ultrasonographic evaluation of myometrial thickness and prediction of a successful external cephalic version.

Science.gov (United States)

Buhimschi, Catalin S; Buhimschi, Irina A; Wehrum, Mark J; Molaskey-Jones, Sherry; Sfakianaki, Anna K; Pettker, Christian M; Thung, Stephen; Campbell, Katherine H; Dulay, Antonette T; Funai, Edmund F; Bahtiyar, Mert O

2011-10-01

To test the hypothesis that myometrial thickness predicts the success of external cephalic version. Abdominal ultrasonographic scans were performed in 114 consecutive pregnant women with breech singletons before an external cephalic version maneuver. Myometrial thickness was measured by a standardized protocol at three sites: the lower segment, midanterior wall, and the fundal uterine wall. Independent variables analyzed in conjunction with myometrial thickness were: maternal age, parity, body mass index, abdominal wall thickness, estimated fetal weight, amniotic fluid index, placental thickness and location, fetal spine position, breech type, and delivery outcomes such as final mode of delivery and birth weight. Successful version was associated with a thicker ultrasonographic fundal myometrium (unsuccessful: 6.7 [5.5-8.4] compared with successful: 7.4 [6.6-9.7] mm, P=.037). Multivariate regression analysis showed that increased fundal myometrial thickness, high amniotic fluid index, and nonfrank breech presentation were the strongest independent predictors of external cephalic version success (Pexternal cephalic versions (fundal myometrial thickness: odds ratio [OR] 2.4, 95% confidence interval [CI] 1.1-5.2, P=.029; amniotic fluid index: OR 2.8, 95% CI 1.3-6.0, P=.008). Combining the two variables resulted in an absolute risk reduction for a failed version of 27.6% (95% CI 7.1-48.1) and a number needed to treat of four (95% CI 2.1-14.2). Fundal myometrial thickness and amniotic fluid index contribute to success of external cephalic version and their evaluation can be easily incorporated in algorithms before the procedure. III.

The usefulness of a computer-aided diagnosis scheme for improving the performance of clinicians to diagnose non-mass lesions on breast ultrasonographic images.

Science.gov (United States)

Shibusawa, Mai; Nakayama, Ryohei; Okanami, Yuko; Kashikura, Yumi; Imai, Nao; Nakamura, Takashi; Kimura, Hiroko; Yamashita, Masako; Hanamura, Noriko; Ogawa, Tomoko

2016-07-01

The purpose of this study was to evaluate the usefulness of a computer-aided diagnosis (CAD) scheme for improving the performance of clinicians to diagnose non-mass lesions appearing as hypoechoic areas on breast ultrasonographic images. The database included 97 ultrasonographic images with hypoechoic areas: 48 benign cases [benign lesion with benign mammary tissue or fibrocystic disease (n = 20), fibroadenoma (n = 11), and intraductal papilloma (n = 17)] and 49 malignant cases [ductal carcinoma in situ (n = 17) and invasive ductal carcinoma (n = 32)]. Seven clinicians, three expert breast surgeons, and four general surgeons participated in the observer study. They were asked their confidence level concerning the possibility of malignancy in all 97 cases with and without the use of the CAD scheme. Receiver operating characteristic (ROC) analysis was performed to evaluate the usefulness of the CAD scheme. The areas under the ROC curve (AUC) improved for all observers when they used the CAD scheme and increased from 0.649 to 0.783 (P = 0.0167). Notably, the AUC for the general surgeon group increased from 0.625 to 0.793 (P = 0.045). This study showed that the performance of clinicians to diagnose non-mass lesions appearing as hypoechoic areas on breast ultrasonographic images was improved by the use of a CAD scheme.

First Time Rapid and Accurate Detection of Massive Number of Metal Absorption Lines in the Early Universe Using Deep Neural Network

Science.gov (United States)

Zhao, Yinan; Ge, Jian; Yuan, Xiaoyong; Li, Xiaolin; Zhao, Tiffany; Wang, Cindy

2018-01-01

Metal absorption line systems in the distant quasar spectra have been used as one of the most powerful tools to probe gas content in the early Universe. The MgII λλ 2796, 2803 doublet is one of the most popular metal absorption lines and has been used to trace gas and global star formation at redshifts between ~0.5 to 2.5. In the past, machine learning algorithms have been used to detect absorption lines systems in the large sky survey, such as Principle Component Analysis, Gaussian Process and decision tree, but the overall detection process is not only complicated, but also time consuming. It usually takes a few months to go through the entire quasar spectral dataset from each of the Sloan Digital Sky Survey (SDSS) data release. In this work, we applied the deep neural network, or “ deep learning” algorithms, in the most recently SDSS DR14 quasar spectra and were able to randomly search 20000 quasar spectra and detect 2887 strong Mg II absorption features in just 9 seconds. Our detection algorithms were verified with previously released DR12 and DR7 data and published Mg II catalog and the detection accuracy is 90%. This is the first time that deep neural network has demonstrated its promising power in both speed and accuracy in replacing tedious, repetitive human work in searching for narrow absorption patterns in a big dataset. We will present our detection algorithms and also statistical results of the newly detected Mg II absorption lines.

ULTRASONOGRAPHIC EVALUATION OF AMOEBIC LIVER ABSCESS

Directory of Open Access Journals (Sweden)

Nagesh

2016-04-01

Full Text Available AIMS To study the role of ultrasonography in the diagnosis, followup, resolution and percutaneous interventions of amoebic liver abscesses. METHODOLOGY 25 patients with 38 amoebic liver abscesses were included in this study. The diagnostic criteria being compatible history, tender and enlarged liver, radiological and ultrasound findings and response to metronidazole therapy. Confirmed cases of amoebic liver abscesses were followed up by ultrasonography till complete resolution. RESULTS The highest incidence of age was seen between 3 rd and 5 th decades (84% with a male sex incidence of 92%, disease preponderance in people belonging to low socioeconomic group and a high incidence among alcoholics. The radiological findings were: Elevation of right dome of diaphragm (56%, restricted diaphragmatic movements (88%, right basal lung changes (48%, right pleural effusion (12%, and indistinct hazy diaphragmatic contour (40%. The ultrasonographic findings were: 87% of the abscesses were located in right lobe, 11% in left lobe and 2% in both lobes. Among the 25 patients, 76% showed solitary and 24% showed multiple abscesses. Of the 38 amoebic abscesses, 79% were hypoechoic, 13% were hyperechoic and 8% were anechoic. 11 patients were subjected for ultrasound-guided aspiration. CONCLUSION Ultrasound is a safe, reliable and non-invasive imaging modality for the diagnosis, followup and percutaneous interventions of amoebic liver abscesses. The sonographic resolution time of amoebic liver abscesses varies from 28 to 286 days.

Ultrasonographic-guided, percutaneous antegarde pyelography: technique and clinical application in the dog and cat

International Nuclear Information System (INIS)

Rivers, B.J.; Walter, P.A.; Polzin, D.J.

1997-01-01

Fluoroscopically guided, percutaneous antegrade pyelography in canine patients has been described previously in the veterinary literature. This report describes the technique with ultrasonographic guidance and its clinical application in the diagnosis of four cases (two dogs, two cats) of obstructive uropathy. The technique provided successful diagnosis of ureteral obstruction in all four cases. No complications were observed in three cases. In one feline case, ureteral obstruction with a blood clot occurred following the procedure; however, it could not be ascertained whether this event represented a complication of the technique

Clinical, ultrasonographic, and roentgenographic study in 134 asymptomatic gallstone carriers; Is oral ursodeoxycholic acid treatment worhtwhile

Energy Technology Data Exchange (ETDEWEB)

Lirussi, F.; Passera, D.; Iemmolo, R.M.; Nassuato, G.; Okolicsanyi, L. (Inst. of Medicine, Univ. of Parma (Italy))

1993-03-01

The authors investigated retrospectively the ultrasonographic and roentgenographic characteristics of the gallstones and the gallbladder in 134 symtom-free carriers and evaluated prospectively the outcome and side effects of 6 to 24 months' ursodeoxycholic acid (UDCA) therapy in 36 individuals with silent stones. Two-thirds of the 134 subjects had multiple stones, and 71 to 75% had stones less than 15 mm in diameter. Gallstone calcification was detected in 13%. A non-functioning gallbladder was observed in 19%, whereas gallbladder contraction was normal in 64 of 76 gallstone carriers. With regard to oral bile acid treatment, complete and partial dissolutions were achieved in 7 and 9 of 33 subjects, respectively (48.5%). Development of a non-functioning gallbladder occurred in 9%, and acquired gallstone calcification was seen in another 15%. It is concluded that: (i) the characteristics of the gallstones and the gallbladder are similar to those observed in symptomatic patients, and (ii) UDCA therapy may be given in selected symptom-free carriers for no more than 6 to 12 months. Thereafter, it does not appear to be cost-effective. 23 refs., 2 figs., 3 tabs.

Volcanic ash detection and retrievals using MODIS data by means of neural networks

Directory of Open Access Journals (Sweden)

M. Picchiani

2011-12-01

Full Text Available Volcanic ash clouds detection and retrieval represent a key issue for aviation safety due to the harming effects on aircraft. A lesson learned from the recent Eyjafjallajokull eruption is the need to obtain accurate and reliable retrievals on a real time basis.

In this work we have developed a fast and accurate Neural Network (NN approach to detect and retrieve volcanic ash cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS data in the Thermal InfraRed (TIR spectral range. Some measurements collected during the 2001, 2002 and 2006 Mt. Etna volcano eruptions have been considered as test cases.

The ash detection and retrievals obtained from the Brightness Temperature Difference (BTD algorithm are used as training for the NN procedure that consists in two separate steps: ash detection and ash mass retrieval. The ash detection is reduced to a classification problem by identifying two classes: "ashy" and "non-ashy" pixels in the MODIS images. Then the ash mass is estimated by means of the NN, replicating the BTD-based model performances. A segmentation procedure has also been tested to remove the false ash pixels detection induced by the presence of high meteorological clouds. The segmentation procedure shows a clear advantage in terms of classification accuracy: the main drawback is the loss of information on ash clouds distal part.

The results obtained are very encouraging; indeed the ash detection accuracy is greater than 90%, while a mean RMSE equal to 0.365 t km⁻² has been obtained for the ash mass retrieval. Moreover, the NN quickness in results delivering makes the procedure extremely attractive in all the cases when the rapid response time of the system is a mandatory requirement.

Fault detection and diagnosis for complex multivariable processes using neural networks

International Nuclear Information System (INIS)

Weerasinghe, M.

1998-06-01

Development of a reliable fault diagnosis method for large-scale industrial plants is laborious and often difficult to achieve due to the complexity of the targeted systems. The main objective of this thesis is to investigate the application of neural networks to the diagnosis of non-catastrophic faults in an industrial nuclear fuel processing plant. The proposed methods were initially developed by application to a simulated chemical process prior to further validation on real industrial data. The diagnosis of faults at a single operating point is first investigated. Statistical data conditioning methods of data scaling and principal component analysis are investigated to facilitate fault classification and reduce the complexity of neural networks. Successful fault diagnosis was achieved with significantly smaller networks than using all process variables as network inputs. Industrial processes often manufacture at various operating points, but demonstrated applications of neural networks for fault diagnosis usually only consider a single (primary) operating point. Developing a standard neural network scheme for fault diagnosis at all operating points would be usually impractical due to the unavailability of suitable training data for less frequently used (secondary) operating points. To overcome this problem, the application of a single neural network for the diagnosis of faults operating at different points is investigated. The data conditioning followed the same techniques as used for the fault diagnosis of a single operating point. The results showed that a single neural network could be successfully used to diagnose faults at operating points other than that it is trained for, and the data conditioning significantly improved the classification. Artificial neural networks have been shown to be an effective tool for process fault diagnosis. However, a main criticism is that details of the procedures taken to reach the fault diagnosis decisions are embedded in

Breast nodules detection in images of ultrasonographic and mammographic simulators; Deteccao de nodulos mamarios em imagens de simuladores ultrassonografico e mamografico

Energy Technology Data Exchange (ETDEWEB)

Marcomini, Karem D.; Schiabel, Homero, E-mail: karem.dm@usp.br [Universidade de Sao Paulo (USP), Sao Carlos, SP (Brazil). Escola de Engenharia. Dept. de Engenharia Eletrica; Carneiro, Antonio Adilton O. [Universidade de Sao Paulo (USP), Ribeirao Preto, SP (Brazil). Faculdade de Filosofia, Ciencias e Letras. Dept. de Fisica

2013-08-15

Due to the high incidence rate of breast cancer in women, many procedures have been developed to assist in the diagnosis and early detection. Mammography and ultrasonography stand out as the main breast imaging techniques. In this context, the schemes of computer-aided diagnosis have provided to the specialist a more accurate and reliable second opinion by minimizing the visual subjectivity inter-observer. Thus, we propose the application of an automated method of segmentation, through the neural network SOM, to provide accurate information regarding the border of the lesion. The tests were employed in 100 mammographic images and 70 sonographic, both cases obtained by simulation. In order to verify the accuracy of the boundaries demarcated by the automatic detector, quantitative measurements were extracted to compare these images with the manually delineated by an experienced radiologist. The proposed technique presented high accuracy and sensitivity, and low error rate in correctly representing the mammographic and sonographic findings. (author)

A new method for brain tumor detection using the Bhattacharyya similarity coefficient, color conversions and neural network

Directory of Open Access Journals (Sweden)

Bahman Mansori

2015-10-01

Full Text Available Background: Magnetic resonance imaging (MRI is widely applied for examination and diagnosis of brain tumors based on its advantages of high resolution in detecting the soft tissues and especially of its harmless radiation damages to human bodies. The goal of the processing of images is automatic segmentation of brain edema and tumors, in different dimensions of the magnetic resonance images. Methods: The proposed method is based on the unsupervised method which discovers the tumor region, if there is any, by analyzing the similarities between two hemispheres and computes the image size of the goal function based on Bhattacharyya coefficient which is used in the next stage to detect the tumor region or some part of it. In this stage, for reducing the color variation, the gray brain image is segmented, then it is turned to gray again. The self-organizing map (SOM neural network is used the segmented brain image is colored and finally the tumor is detected by matching the detected region and the colored image. This method is proposed to analyze MRI images for discovering brain tumors, and done in Bu Ali Sina University, Hamedan, Iran, in 2014. Results: The results for 30 randomly selected images from data bank of MRI center in Hamedan was compared with manually segmentation of experts. The results showed that, our proposed method had the accuracy of more than 94% at Jaccard similarity index (JSI, 97% at Dice similarity score (DSS, and 98% and 99% at two measures of specificity and sensitivity. Conclusion: The experimental results showed that it was satisfactory and can be used in automatic separation of tumor from normal brain tissues and therefore it can be used in practical applications. The results showed that the use of SOM neural network to classify useful magnetic resonance imaging of the brain and demonstrated a good performance.

Uterine Artery Embolization Combined with Local Methotrexate and Systemic Methotrexate for Treatment of Cesarean Scar Pregnancy with Different Ultrasonographic Pattern

International Nuclear Information System (INIS)

Lian Fan; Wang Yu; Chen Wei; Li Jiaping; Zhan Zhongping; Ye Yujin; Zhu, Yunxiao; Huang Jia; Xu Hanshi; Yang Xiuyan; Liang Liuqin; Yang Jianyong

2012-01-01

Purpose: This study was designed to compare the effectiveness of systemic methotrexate (MTX) with uterine artery embolization (UAE) combined with local MTX for the treatment of cesarean scar pregnancy (CSP) with different ultrasonographic pattern, and to indicate the preferable therapy in CSP patients. Methods: The results of 21 CSP cases were reviewed. All subjects were initially administrated with systemic MTX (50 mg/m 2 body surface area). UAE combined with local MTX was added to the patients who had failed systemic MTX. The transvaginal ultrasonography data were retrospectively assessed, and two different ultrasonographic patterns were found: surface implantation and deep implantation of amniotic sac. The management and its effectiveness for patients with the two ultrasonographic patterns were studied retrospectively. Ultrasound scan and serum β-hCG were monitored during follow-up. Data were analyzed with the Student’s t test. Results: Nine patients were successfully treated with systemic MTX. The remaining 12 cases were successfully treated with additional UAE combined with local MTX. According to the classification by Vial et al. of CSP on ultrasonography, most surface implanted CSPs (8/11, 72.7%) could be successfully treated with systemic MTX, whereas most deeply implanted CSPs (9/10, 90%) had failed systemic MTX but still could be successfully treated with additional UAE combined with local MTX. All patients recovered without severe side effects. Most patients with a future desire for reproduction achieved subsequent pregnancy. Conclusions: For CSP patients suitable for nonsurgical treatment, UAE combined with local MTX would be the superior option compared with systemic MTX in the cases with deep implantation of amniotic sac.

Neural Bases of Unconscious Error Detection in a Chinese Anagram Solution Task: Evidence from ERP Study.

Directory of Open Access Journals (Sweden)

Hua-Zhan Yin

Full Text Available In everyday life, error monitoring and processing are important for improving ongoing performance in response to a changing environment. However, detecting an error is not always a conscious process. The temporal activation patterns of brain areas related to cognitive control in the absence of conscious awareness of an error remain unknown. In the present study, event-related potentials (ERPs in the brain were used to explore the neural bases of unconscious error detection when subjects solved a Chinese anagram task. Our ERP data showed that the unconscious error detection (UED response elicited a more negative ERP component (N2 than did no error (NE and detect error (DE responses in the 300-400-ms time window, and the DE elicited a greater late positive component (LPC than did the UED and NE in the 900-1200-ms time window after the onset of the anagram stimuli. Taken together with the results of dipole source analysis, the N2 (anterior cingulate cortex might reflect unconscious/automatic conflict monitoring, and the LPC (superior/medial frontal gyrus might reflect conscious error recognition.

Estimation of neural energy in microelectrode signals

Science.gov (United States)

Gaumond, R. P.; Clement, R.; Silva, R.; Sander, D.

2004-09-01

We considered the problem of determining the neural contribution to the signal recorded by an intracortical electrode. We developed a linear least-squares approach to determine the energy fraction of a signal attributable to an arbitrary number of autocorrelation-defined signals buried in noise. Application of the method requires estimation of autocorrelation functions Rap(tgr) characterizing the action potential (AP) waveforms and Rn(tgr) characterizing background noise. This method was applied to the analysis of chronically implanted microelectrode signals from motor cortex of rat. We found that neural (AP) energy consisted of a large-signal component which grows linearly with the number of threshold-detected neural events and a small-signal component unrelated to the count of threshold-detected AP signals. The addition of pseudorandom noise to electrode signals demonstrated the algorithm's effectiveness for a wide range of noise-to-signal energy ratios (0.08 to 39). We suggest, therefore, that the method could be of use in providing a measure of neural response in situations where clearly identified spike waveforms cannot be isolated, or in providing an additional 'background' measure of microelectrode neural activity to supplement the traditional AP spike count.

Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network.

Science.gov (United States)

Charron, Odelin; Lallement, Alex; Jarnet, Delphine; Noblet, Vincent; Clavier, Jean-Baptiste; Meyer, Philippe

2018-04-01

Stereotactic treatments are today the reference techniques for the irradiation of brain metastases in radiotherapy. The dose per fraction is very high, and delivered in small volumes (diameter convolutional neural network (DeepMedic) to detect and segment brain metastases on MRI. At first, we sought to adapt the network parameters to brain metastases. We then explored the single or combined use of different MRI modalities, by evaluating network performance in terms of detection and segmentation. We also studied the interest of increasing the database with virtual patients or of using an additional database in which the active parts of the metastases are separated from the necrotic parts. Our results indicated that a deep network approach is promising for the detection and the segmentation of brain metastases on multimodal MRI. Copyright © 2018 Elsevier Ltd. All rights reserved.

Rhesus monkey neural stem cell transplantation promotes neural regeneration in rats with hippocampal lesions

Directory of Open Access Journals (Sweden)

Li-juan Ye

2016-01-01

Full Text Available Rhesus monkey neural stem cells are capable of differentiating into neurons and glial cells. Therefore, neural stem cell transplantation can be used to promote functional recovery of the nervous system. Rhesus monkey neural stem cells (1 × 105 cells/μL were injected into bilateral hippocampi of rats with hippocampal lesions. Confocal laser scanning microscopy demonstrated that green fluorescent protein-labeled transplanted cells survived and grew well. Transplanted cells were detected at the lesion site, but also in the nerve fiber-rich region of the cerebral cortex and corpus callosum. Some transplanted cells differentiated into neurons and glial cells clustering along the ventricular wall, and integrated into the recipient brain. Behavioral tests revealed that spatial learning and memory ability improved, indicating that rhesus monkey neural stem cells noticeably improve spatial learning and memory abilities in rats with hippocampal lesions.

Fault diagnosis system of electromagnetic valve using neural network filter

International Nuclear Information System (INIS)

Hayashi, Shoji; Odaka, Tomohiro; Kuroiwa, Jousuke; Ogura, Hisakazu

2008-01-01

This paper is concerned with the gas leakage fault detection of electromagnetic valve using a neural network filter. In modern plants, the ability to detect and identify gas leakage faults is becoming increasingly important. The main difficulty in detecting gas leakage faults by sound signals lies in the fact that the practical plants are usually very noisy. To solve this difficulty, a neural network filter is used to eliminate background noise and raise the signal noise ratio of the sound signal. The background noise is assumed as a dynamic system, and an accurate mathematical model of the dynamic system can be established using a neural network filter. The predicted error between predicted values and practical ones constitutes the output of the filter. If the predicted error is zero, then there is no leakage. If the predicted error is greater than a certain value, then there is a leakage fault. Through application to practical pneumatic systems, it is verified that the neural network filter was effective in gas leakage detection. (author)

A study of reactor monitoring method with neural network

Energy Technology Data Exchange (ETDEWEB)

Nabeshima, Kunihiko [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

2001-03-01

The purpose of this study is to investigate the methodology of Nuclear Power Plant (NPP) monitoring with neural networks, which create the plant models by the learning of the past normal operation patterns. The concept of this method is to detect the symptom of small anomalies by monitoring the deviations between the process signals measured from an actual plant and corresponding output signals from the neural network model, which might not be equal if the abnormal operational patterns are presented to the input of the neural network. Auto-associative network, which has same output as inputs, can detect an kind of anomaly condition by using normal operation data only. The monitoring tests of the feedforward neural network with adaptive learning were performed using the PWR plant simulator by which many kinds of anomaly conditions can be easily simulated. The adaptively trained feedforward network could follow the actual plant dynamics and the changes of plant condition, and then find most of the anomalies much earlier than the conventional alarm system during steady state and transient operations. Then the off-line and on-line test results during one year operation at the actual NPP (PWR) showed that the neural network could detect several small anomalies which the operators or the conventional alarm system didn't noticed. Furthermore, the sensitivity analysis suggests that the plant models by neural networks are appropriate. Finally, the simulation results show that the recurrent neural network with feedback connections could successfully model the slow behavior of the reactor dynamics without adaptive learning. Therefore, the recurrent neural network with adaptive learning will be the best choice for the actual reactor monitoring system. (author)

Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat-5 Imagery

Directory of Open Access Journals (Sweden)

Jeong-In Hwang

2017-09-01

Full Text Available For ship detection, X-band synthetic aperture radar (SAR imagery provides very useful data, in that ship targets look much brighter than surrounding sea clutter due to the corner-reflection effect. However, there are many phenomena which bring out false detection in the SAR image, such as noise of background, ghost phenomena, side-lobe effects and so on. Therefore, when ship-detection algorithms are carried out, we should consider these effects and mitigate them to acquire a better result. In this paper, we propose an efficient method to detect ship targets from X-band Kompsat-5 SAR imagery using the artificial neural network (ANN. The method produces the ship-probability map using ANN, and then detects ships from the ship-probability map by using a threshold value. For the purpose of getting an improved ship detection, we strived to produce optimal input layers used for ANN. In order to reduce phenomena related to the false detections, the non-local (NL-means filter and median filter were utilized. The NL-means filter effectively reduced noise on SAR imagery without smoothing edges of the objects, and the median filter was used to remove ship targets in SAR imagery. Through the filtering approaches, we generated two input layers from a Kompsat-5 SAR image, and created a ship-probability map via ANN from the two input layers. When the threshold value of 0.67 was imposed on the ship-probability map, the result of ship detection from the ship-probability map was a 93.9% recall, 98.7% precision and 6.1% false alarm rate. Therefore, the proposed method was successfully applied to the ship detection from the Kompsat-5 SAR image.

Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.

Science.gov (United States)

Yousefi, Mina; Krzyżak, Adam; Suen, Ching Y

2018-05-01

Digital breast tomosynthesis (DBT) was developed in the field of breast cancer screening as a new tomographic technique to minimize the limitations of conventional digital mammography breast screening methods. A computer-aided detection (CAD) framework for mass detection in DBT has been developed and is described in this paper. The proposed framework operates on a set of two-dimensional (2D) slices. With plane-to-plane analysis on corresponding 2D slices from each DBT, it automatically learns complex patterns of 2D slices through a deep convolutional neural network (DCNN). It then applies multiple instance learning (MIL) with a randomized trees approach to classify DBT images based on extracted information from 2D slices. This CAD framework was developed and evaluated using 5040 2D image slices derived from 87 DBT volumes. The empirical results demonstrate that this proposed CAD framework achieves much better performance than CAD systems that use hand-crafted features and deep cardinality-restricted Bolzmann machines to detect masses in DBTs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Artificial neural network techniques to improve the ability of optical coherence tomography to detect optic neuritis.

Science.gov (United States)

Garcia-Martin, Elena; Herrero, Raquel; Bambo, Maria P; Ara, Jose R; Martin, Jesus; Polo, Vicente; Larrosa, Jose M; Garcia-Feijoo, Julian; Pablo, Luis E

2015-01-01

To analyze the ability of Spectralis optical coherence tomography (OCT) to detect multiple sclerosis (MS) and to distinguish MS eyes with antecedent optic neuritis (ON). To analyze the capability of artificial neural network (ANN) techniques to improve the diagnostic precision. MS patients and controls were enrolled (n = 217). OCT was used to determine the 768 retinal nerve fiber layer thicknesses. Sensitivity and specificity were evaluated to test the ability of OCT to discriminate between MS and healthy eyes, and between MS with and without antecedent ON using ANN. Using ANN technique multilayer perceptrons, OCT could detect MS with a sensitivity of 89.3%, a specificity of 87.6%, and a diagnostic precision of 88.5%. Compared with the OCT-provided parameters, the ANN had a better sensitivity-specificity balance. ANN technique improves the capability of Spectralis OCT to detect MS disease and to distinguish MS eyes with or without antecedent ON.

A Neural Network Approach for Building An Obstacle Detection Model by Fusion of Proximity Sensors Data

Science.gov (United States)

Peralta, Emmanuel; Vargas, Héctor; Hermosilla, Gabriel

2018-01-01

Proximity sensors are broadly used in mobile robots for obstacle detection. The traditional calibration process of this kind of sensor could be a time-consuming task because it is usually done by identification in a manual and repetitive way. The resulting obstacles detection models are usually nonlinear functions that can be different for each proximity sensor attached to the robot. In addition, the model is highly dependent on the type of sensor (e.g., ultrasonic or infrared), on changes in light intensity, and on the properties of the obstacle such as shape, colour, and surface texture, among others. That is why in some situations it could be useful to gather all the measurements provided by different kinds of sensor in order to build a unique model that estimates the distances to the obstacles around the robot. This paper presents a novel approach to get an obstacles detection model based on the fusion of sensors data and automatic calibration by using artificial neural networks. PMID:29495338

Alcoholism detection in magnetic resonance imaging by Haar wavelet transform and back propagation neural network

Science.gov (United States)

Yu, Yali; Wang, Mengxia; Lima, Dimas

2018-04-01

In order to develop a novel alcoholism detection method, we proposed a magnetic resonance imaging (MRI)-based computer vision approach. We first use contrast equalization to increase the contrast of brain slices. Then, we perform Haar wavelet transform and principal component analysis. Finally, we use back propagation neural network (BPNN) as the classification tool. Our method yields a sensitivity of 81.71±4.51%, a specificity of 81.43±4.52%, and an accuracy of 81.57±2.18%. The Haar wavelet gives better performance than db4 wavelet and sym3 wavelet.

Pre-trained convolutional neural networks as feature extractors for tuberculosis detection.

Science.gov (United States)

Lopes, U K; Valiati, J F

2017-10-01

It is estimated that in 2015, approximately 1.8 million people infected by tuberculosis died, most of them in developing countries. Many of those deaths could have been prevented if the disease had been detected at an earlier stage, but the most advanced diagnosis methods are still cost prohibitive for mass adoption. One of the most popular tuberculosis diagnosis methods is the analysis of frontal thoracic radiographs; however, the impact of this method is diminished by the need for individual analysis of each radiography by properly trained radiologists. Significant research can be found on automating diagnosis by applying computational techniques to medical images, thereby eliminating the need for individual image analysis and greatly diminishing overall costs. In addition, recent improvements on deep learning accomplished excellent results classifying images on diverse domains, but its application for tuberculosis diagnosis remains limited. Thus, the focus of this work is to produce an investigation that will advance the research in the area, presenting three proposals to the application of pre-trained convolutional neural networks as feature extractors to detect the disease. The proposals presented in this work are implemented and compared to the current literature. The obtained results are competitive with published works demonstrating the potential of pre-trained convolutional networks as medical image feature extractors. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effect of ionizing radiation on the differentiation of neural stem cells

International Nuclear Information System (INIS)

Liu Ping; Tu Yu

2010-01-01

In order to investigate the effect of ionizing radiation on neural stem cells differentiation, we cultured neural stem cells of newborn rat in serum-free media containing EGF or bFGF. The neural stem cells were divided into 4 groups, which were irradiated by γ-rays with doses of 0, 0.5, 1, and 2 Gy. The irradiated cells were cultured under the same condition for 7 days, and the nestin content of neural stem cell was detected by immunofluorescence. The same method was carried out with irradiated cells in the culture medium after removing EGF, bFGF for 7 days, NSE and GFAP expression content and nestin were also detected by immunofluorescence. It has been found that the irradiated neural stem cells can express less nestin and differentiate more neurons compared to that of control group. Results show that ionizing radiation can induce the differentiation of the neural stem cells and make the neural stem cells differentiate more neuron. (authors)

Detecting danger labels with RAM-based neural networks

DEFF Research Database (Denmark)

Jørgensen, T.M.; Christensen, S.S.; Andersen, A.W.

1996-01-01

An image processing system for the automatic location of danger labels on the back of containers is presented. The system uses RAM-based neural networks to locate and classify labels after a pre-processing step involving specially designed non-linear edge filters and RGB-to-HSV conversion. Result...

Ultrasonographic evaluation to diagnose hepatic lipidosis in Egyptian Zaraibi goats with vitamin B12 deficiency

OpenAIRE

Sabry A. El-Khodery; Hussein S. Hussein; Mohamed E. El-Boshy; Medhat N. Nassif

2011-01-01

As little is known about the ultrasonographic features of hepatic lipidosis (white liver disease) in goats, this study was undertaken to evaluate the use of ultrasound for the diagnosis of hepatic lipidosis associated with vitamin B12 (cyanocobalamin) deficiency in Egyptian Zaraibi goats. A total of 38 goats (28 with weight loss, diarrhoea and anaemia and 10 clinically healthy) were studied. Twenty-one goats were demonstrated to have cobalt and cyanocobalamin deficiency (0.33 ± 0.12 μmol/l an...

An artificial neural network method for lumen and media-adventitia border detection in IVUS.

Science.gov (United States)

Su, Shengran; Hu, Zhenghui; Lin, Qiang; Hau, William Kongto; Gao, Zhifan; Zhang, Heye

2017-04-01

Intravascular ultrasound (IVUS) has been well recognized as one powerful imaging technique to evaluate the stenosis inside the coronary arteries. The detection of lumen border and media-adventitia (MA) border in IVUS images is the key procedure to determine the plaque burden inside the coronary arteries, but this detection could be burdensome to the doctor because of large volume of the IVUS images. In this paper, we use the artificial neural network (ANN) method as the feature learning algorithm for the detection of the lumen and MA borders in IVUS images. Two types of imaging information including spatial, neighboring features were used as the input data to the ANN method, and then the different vascular layers were distinguished accordingly through two sparse auto-encoders and one softmax classifier. Another ANN was used to optimize the result of the first network. In the end, the active contour model was applied to smooth the lumen and MA borders detected by the ANN method. The performance of our approach was compared with the manual drawing method performed by two IVUS experts on 461 IVUS images from four subjects. Results showed that our approach had a high correlation and good agreement with the manual drawing results. The detection error of the ANN method close to the error between two groups of manual drawing result. All these results indicated that our proposed approach could efficiently and accurately handle the detection of lumen and MA borders in the IVUS images. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neural network classifier of attacks in IP telephony

Science.gov (United States)

Safarik, Jakub; Voznak, Miroslav; Mehic, Miralem; Partila, Pavol; Mikulec, Martin

2014-05-01

Various types of monitoring mechanism allow us to detect and monitor behavior of attackers in VoIP networks. Analysis of detected malicious traffic is crucial for further investigation and hardening the network. This analysis is typically based on statistical methods and the article brings a solution based on neural network. The proposed algorithm is used as a classifier of attacks in a distributed monitoring network of independent honeypot probes. Information about attacks on these honeypots is collected on a centralized server and then classified. This classification is based on different mechanisms. One of them is based on the multilayer perceptron neural network. The article describes inner structure of used neural network and also information about implementation of this network. The learning set for this neural network is based on real attack data collected from IP telephony honeypot called Dionaea. We prepare the learning set from real attack data after collecting, cleaning and aggregation of this information. After proper learning is the neural network capable to classify 6 types of most commonly used VoIP attacks. Using neural network classifier brings more accurate attack classification in a distributed system of honeypots. With this approach is possible to detect malicious behavior in a different part of networks, which are logically or geographically divided and use the information from one network to harden security in other networks. Centralized server for distributed set of nodes serves not only as a collector and classifier of attack data, but also as a mechanism for generating a precaution steps against attacks.

Preoperative ultrasonographic findings of internal jugular veins and carotid arteries in kidney transplant recipients.

Science.gov (United States)

Choi, Ji Won; Kim, Gaab Soo; Lee, Seung Won; Park, Jeong Bo; Lee, Jeong Jin; Ko, Justin Sangwook

2016-08-01

Hemodialysis via the internal jugular vein (IJV) has been widely used for patients with end stage renal disease (ESRD) patients, as they have a higher risk of arterial diseases. We investigated the ultrasonographic findings of the IJV and carotid artery (CA) in recipients of kidney transplantation (KT) and identified factors influencing IJV/CA abnormalities. We enrolled 120 adult KT recipients. Patients in group A (n = 57) had a history of IJV hemodialysis, while those in group B (n = 63) were not yet on dialysis or undergoing dialysis methods not involving the IJV. The day before surgery, we evaluated the state of the IJV and CA using ultrasonography. We followed patients with IJV stenosis for six months after KT. Ultrasonography revealed that four patients (7%) in group A had IJV abnormalities, while no patients in group B had abnormalities (P = 0.118). Of the four patients with abnormalities, one with 57.4% stenosis normalized during follow- up. However, another patient with 90.1% stenosis progressed to occlusion, while the two patients with total occlusion remained the same. Twenty patients in group A (n = 11) and B (n = 9) had several CA abnormalities (P = 0.462). Upon multivariate analysis with stepwise selection, height and age were significantly correlated with IJV stenosis (P = 0.043, odds ratio = 0.9) and CA abnormality (P = 0.012, odds ratio = 1.1), respectively. IJV abnormalities (especially with a history of IJV hemodialysis) and CA abnormalities may be present in ESRD patients. Therefore, we recommend ultrasonographic evaluation before catheterization.

Convolutional neural network guided blue crab knuckle detection for autonomous crab meat picking machine

Science.gov (United States)

Wang, Dongyi; Vinson, Robert; Holmes, Maxwell; Seibel, Gary; Tao, Yang

2018-04-01

The Atlantic blue crab is among the highest-valued seafood found in the American Eastern Seaboard. Currently, the crab processing industry is highly dependent on manual labor. However, there is great potential for vision-guided intelligent machines to automate the meat picking process. Studies show that the back-fin knuckles are robust features containing information about a crab's size, orientation, and the position of the crab's meat compartments. Our studies also make it clear that detecting the knuckles reliably in images is challenging due to the knuckle's small size, anomalous shape, and similarity to joints in the legs and claws. An accurate and reliable computer vision algorithm was proposed to detect the crab's back-fin knuckles in digital images. Convolutional neural networks (CNNs) can localize rough knuckle positions with 97.67% accuracy, transforming a global detection problem into a local detection problem. Compared to the rough localization based on human experience or other machine learning classification methods, the CNN shows the best localization results. In the rough knuckle position, a k-means clustering method is able to further extract the exact knuckle positions based on the back-fin knuckle color features. The exact knuckle position can help us to generate a crab cutline in XY plane using a template matching method. This is a pioneering research project in crab image analysis and offers advanced machine intelligence for automated crab processing.

Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography.

Science.gov (United States)

Nakao, Takahiro; Hanaoka, Shouhei; Nomura, Yukihiro; Sato, Issei; Nemoto, Mitsutaka; Miki, Soichiro; Maeda, Eriko; Yoshikawa, Takeharu; Hayashi, Naoto; Abe, Osamu

2018-04-01

The usefulness of computer-assisted detection (CAD) for detecting cerebral aneurysms has been reported; therefore, the improved performance of CAD will help to detect cerebral aneurysms. To develop a CAD system for intracranial aneurysms on unenhanced magnetic resonance angiography (MRA) images based on a deep convolutional neural network (CNN) and a maximum intensity projection (MIP) algorithm, and to demonstrate the usefulness of the system by training and evaluating it using a large dataset. Retrospective study. There were 450 cases with intracranial aneurysms. The diagnoses of brain aneurysms were made on the basis of MRA, which was performed as part of a brain screening program. Noncontrast-enhanced 3D time-of-flight (TOF) MRA on 3T MR scanners. In our CAD, we used a CNN classifier that predicts whether each voxel is inside or outside aneurysms by inputting MIP images generated from a volume of interest (VOI) around the voxel. The CNN was trained in advance using manually inputted labels. We evaluated our method using 450 cases with intracranial aneurysms, 300 of which were used for training, 50 for parameter tuning, and 100 for the final evaluation. Free-response receiver operating characteristic (FROC) analysis. Our CAD system detected 94.2% (98/104) of aneurysms with 2.9 false positives per case (FPs/case). At a sensitivity of 70%, the number of FPs/case was 0.26. We showed that the combination of a CNN and an MIP algorithm is useful for the detection of intracranial aneurysms. 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:948-953. © 2017 International Society for Magnetic Resonance in Medicine.

Relationship among vaginal palpation, vaginal squeeze pressure, electromyographic and ultrasonographic variables of female pelvic floor muscles

Directory of Open Access Journals (Sweden)

Vanessa S. Pereira

2014-10-01

Full Text Available Background: The proper evaluation of the pelvic floor muscles (PFM is essential for choosing the correct treatment. Currently, there is no gold standard for the assessment of female PFM function. Objective: To determine the correlation between vaginal palpation, vaginal squeeze pressure, and electromyographic and ultrasonographic variables of the female PFM. Method: This cross-sectional study evaluated 80 women between 18 and 35 years of age who were nulliparous and had no pelvic floor dysfunction. PFM function was assessed based on digital palpation, vaginal squeeze pressure, electromyographic activity, bilateral diameter of the bulbocavernosus muscles and the amount of bladder neck movement during voluntary PFM contraction using transperineal bi-dimensional ultrasound. The Pearson correlation was used for statistical analysis (p<0.05. Results: There was a strong positive correlation between PFM function and PFM contraction pressure (0.90. In addition, there was a moderate positive correlation between these two variables and PFM electromyographic activity (0.59 and 0.63, respectively and movement of the bladder neck in relation to the pubic symphysis (0.51 and 0.60, respectively. Conclusions: This study showed that there was a correlation between vaginal palpation, vaginal squeeze pressure, and electromyographic and ultrasonographic variables of the PFM in nulliparous women. The strong correlation between digital palpation and PFM contraction pressure indicated that perineometry could easily be replaced by PFM digital palpation in the absence of equipment.

A Feasibility Study for Perioperative Ventricular Tachycardia Prognosis and Detection and Noise Detection Using a Neural Network and Predictive Linear Operators

Science.gov (United States)

Moebes, T. A.

1994-01-01

To locate the accessory pathway(s) in preexicitation syndromes, epicardial and endocardial ventricular mapping is performed during anterograde ventricular activation via accessory pathway(s) from data originally received in signal form. As the number of channels increases, it is pertinent that more automated detection of coherent/incoherent signals is achieved as well as the prediction and prognosis of ventricular tachywardia (VT). Today's computers and computer program algorithms are not good in simple perceptual tasks such as recognizing a pattern or identifying a sound. This discrepancy, among other things, has been a major motivating factor in developing brain-based, massively parallel computing architectures. Neural net paradigms have proven to be effective at pattern recognition tasks. In signal processing, the picking of coherent/incoherent signals represents a pattern recognition task for computer systems. The picking of signals representing the onset ot VT also represents such a computer task. We attacked this problem by defining four signal attributes for each potential first maximal arrival peak and one signal attribute over the entire signal as input to a back propagation neural network. One attribute was the predicted amplitude value after the maximum amplitude over a data window. Then, by using a set of known (user selected) coherent/incoherent signals, and signals representing the onset of VT, we trained the back propagation network to recognize coherent/incoherent signals, and signals indicating the onset of VT. Since our output scheme involves a true or false decision, and since the output unit computes values between 0 and 1, we used a Fuzzy Arithmetic approach to classify data as coherent/incoherent signals. Furthermore, a Mean-Square Error Analysis was used to determine system stability. The neural net based picking coherent/incoherent signal system achieved high accuracy on picking coherent/incoherent signals on different patients. The system

Ultrasonographic appearance of early embryonic mortality in buffalo (Bubalus bubalis

Directory of Open Access Journals (Sweden)

Giuseppe Catone

2010-01-01

Full Text Available Embryonic mortality is one of the main causes responsible of the decline in fertility that occurs in buffaloes during periods of increasing daylight length (out sexual breeding season. Transrectal ultrasonography for pregnancy diagnosis offers some advantages over palpation per rectum: earlier diagnosis of pregnancy/non-pregnancy, determination of embryo/fetus viability, reduction of misdiagnosis, and reduction of .potential. iatrogenic embryo/fetal attrition. Non pregnant buffaloes on Day 25 after AI showed higher Resistive Index (RI (P<0.05 and Pulsatility Index (P=0.07 values, registered on CL on Days 10 after AI, compared to pregnant buffaloes. RI values were significantly higher (P=0.02 in non pregnant buffaloes also on Day 45 after AI. Colour Doppler sonography could be used to gain specific information relating to the ovarian blood flow in predicting early embryonic loss and to describe the ultrasonographic features of early embryonic death in buffaloes.

Neural Networks

International Nuclear Information System (INIS)

Smith, Patrick I.

2003-01-01

Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

Ultrasonographic evidence of colonic mesenteric vessels as an indicator of right dorsal displacement of the large colon in 13 horses.

Science.gov (United States)

Grenager, N S; Durham, M G

2011-08-01

This report describes the use of ultrasound to diagnose right dorsal displacement of the large colon (RDDLC) in 13 horses prior to surgery. Horses had ultrasonographic examinations performed of the right lateroventral aspect of the abdomen upon admission to the hospital with a 2-5 MHz broadband curvilinear sector scanning transducer after alcohol was used to wet the hair. First, the caecal vessels were identified in the right flank and followed medially and cranially. Next, each intercostal space, from caudal to cranial, was scanned from dorsal to ventral evaluating for abnormally-located mesenteric vessels associated with the large colon. Abnormally-located mesenteric vessels associated with the large colon, distinct from the caecal vessels, were identified in 13 of 23 horses with a diagnosis of RDDLC obtained at exploratory laparotomy. In horses, ultrasonographic visualisation of mesenteric vessels along the right lateral abdomen, dorsal to the costochondral junction in at least 2 intercostal spaces, distinct from the caecal vessels, is consistent with a surgical diagnosis of RDDLC. © 2011 EVJ Ltd.

The harmonics detection method based on neural network applied ...

African Journals Online (AJOL)

user

Keywords: Artificial Neural Networks (ANN), p-q theory, (SAPF), Harmonics, Total ..... Genetic algorithm-based self-learning fuzzy PI controller for shunt active filter, ... Verification of global optimality of the OFC active power filters by means of ...

Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors.

Science.gov (United States)

Kim, Jong Hyun; Hong, Hyung Gil; Park, Kang Ryoung

2017-05-08

Because intelligent surveillance systems have recently undergone rapid growth, research on accurately detecting humans in videos captured at a long distance is growing in importance. The existing research using visible light cameras has mainly focused on methods of human detection for daytime hours when there is outside light, but human detection during nighttime hours when there is no outside light is difficult. Thus, methods that employ additional near-infrared (NIR) illuminators and NIR cameras or thermal cameras have been used. However, in the case of NIR illuminators, there are limitations in terms of the illumination angle and distance. There are also difficulties because the illuminator power must be adaptively adjusted depending on whether the object is close or far away. In the case of thermal cameras, their cost is still high, which makes it difficult to install and use them in a variety of places. Because of this, research has been conducted on nighttime human detection using visible light cameras, but this has focused on objects at a short distance in an indoor environment or the use of video-based methods to capture multiple images and process them, which causes problems related to the increase in the processing time. To resolve these problems, this paper presents a method that uses a single image captured at night on a visible light camera to detect humans in a variety of environments based on a convolutional neural network. Experimental results using a self-constructed Dongguk night-time human detection database (DNHD-DB1) and two open databases (Korea advanced institute of science and technology (KAIST) and computer vision center (CVC) databases), as well as high-accuracy human detection in a variety of environments, show that the method has excellent performance compared to existing methods.

Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors

Directory of Open Access Journals (Sweden)

Jong Hyun Kim

2017-05-01

Full Text Available Because intelligent surveillance systems have recently undergone rapid growth, research on accurately detecting humans in videos captured at a long distance is growing in importance. The existing research using visible light cameras has mainly focused on methods of human detection for daytime hours when there is outside light, but human detection during nighttime hours when there is no outside light is difficult. Thus, methods that employ additional near-infrared (NIR illuminators and NIR cameras or thermal cameras have been used. However, in the case of NIR illuminators, there are limitations in terms of the illumination angle and distance. There are also difficulties because the illuminator power must be adaptively adjusted depending on whether the object is close or far away. In the case of thermal cameras, their cost is still high, which makes it difficult to install and use them in a variety of places. Because of this, research has been conducted on nighttime human detection using visible light cameras, but this has focused on objects at a short distance in an indoor environment or the use of video-based methods to capture multiple images and process them, which causes problems related to the increase in the processing time. To resolve these problems, this paper presents a method that uses a single image captured at night on a visible light camera to detect humans in a variety of environments based on a convolutional neural network. Experimental results using a self-constructed Dongguk night-time human detection database (DNHD-DB1 and two open databases (Korea advanced institute of science and technology (KAIST and computer vision center (CVC databases, as well as high-accuracy human detection in a variety of environments, show that the method has excellent performance compared to existing methods.

Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining.

Science.gov (United States)

Tang, Tianyu; Zhou, Shilin; Deng, Zhipeng; Zou, Huanxin; Lei, Lin

2017-02-10

Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well. In this study, an improved detection method based on Faster R-CNN is proposed in order to accomplish the two challenges mentioned above. Firstly, to improve the recall, we employ a hyper region proposal network (HRPN) to extract vehicle-like targets with a combination of hierarchical feature maps. Then, we replace the classifier after RPN by a cascade of boosted classifiers to verify the candidate regions, aiming at reducing false detection by negative example mining. We evaluate our method on the Munich vehicle dataset and the collected vehicle dataset, with improvements in accuracy and robustness compared to existing methods.

Color Doppler Ultrasonographic Findings of Vascular Leiomyoma: Pathologic Correlation

Energy Technology Data Exchange (ETDEWEB)

Ko, Ji Young; Koh, Sung Hye; Min, Soo Kee; Choi, A Lam; Jang, Kyung Mi; Lee, Yul; Lee, Kwan Seop; Lee, Hyun; Sohn, Jeong Hee [Hallym University Sacred Heart Hospital, Anyang (Korea, Republic of); Kim, Sam Soo [Kangwon National University College of Medicine, Chuncheon (Korea, Republic of)

2009-12-15

To evaluate the distribution of color flow signals on color Doppler ultrasonography of vascular leiomyomas and to correlate them with pathologic findings. We retrospectively analyzed color Doppler ultrasonographic images and pathologic slides of six vascular leiomyomas. We classified the patterns of distribution of color flow signals into localized compact cluster types and non-cluster types, and the pathologic findings into three subtypes: solid, venous and cavernous. All cases showed well-defined homogenous hypoechoic subcutaneous masses on gray-scale ultrasonography. Three cases showed localized compact cluster types on color Doppler ultrasonography, one in each subtype (solid, venous and cavernous). For the three non-cluster types, again there was on in each subtype. In addition, on pathologic analysis the zone of the localized compact cluster of color flow signals coincided with a cluster of larger, vascular caliber masses. Localized compact clusters of color flow signals on color Doppler ultrasonography were seen in 50% of our cases and correlated with a cluster of larger vascular caliber in the mass. But the pattern of distribution of color flows didn't show a correlation with pathologic type

Color Doppler Ultrasonographic Findings of Vascular Leiomyoma: Pathologic Correlation

International Nuclear Information System (INIS)

Ko, Ji Young; Koh, Sung Hye; Min, Soo Kee; Choi, A Lam; Jang, Kyung Mi; Lee, Yul; Lee, Kwan Seop; Lee, Hyun; Sohn, Jeong Hee; Kim, Sam Soo

2009-01-01

To evaluate the distribution of color flow signals on color Doppler ultrasonography of vascular leiomyomas and to correlate them with pathologic findings. We retrospectively analyzed color Doppler ultrasonographic images and pathologic slides of six vascular leiomyomas. We classified the patterns of distribution of color flow signals into localized compact cluster types and non-cluster types, and the pathologic findings into three subtypes: solid, venous and cavernous. All cases showed well-defined homogenous hypoechoic subcutaneous masses on gray-scale ultrasonography. Three cases showed localized compact cluster types on color Doppler ultrasonography, one in each subtype (solid, venous and cavernous). For the three non-cluster types, again there was on in each subtype. In addition, on pathologic analysis the zone of the localized compact cluster of color flow signals coincided with a cluster of larger, vascular caliber masses. Localized compact clusters of color flow signals on color Doppler ultrasonography were seen in 50% of our cases and correlated with a cluster of larger vascular caliber in the mass. But the pattern of distribution of color flows didn't show a correlation with pathologic type

Color Doppler Ultrasonographic Features of Hashimoto's Thyroiditis

Energy Technology Data Exchange (ETDEWEB)

Lee, Joo Hyuk; Kim, Mie Young; Rho, Eun Jin; Yi, Jeong Geun; Han, Chun Hwan [Kangnam General Hospital Public Corporation, Seoul (Korea, Republic of); Hwang, Hee Yong [Choong Ang Gil Hospital, Incheon (Korea, Republic of)

1995-06-15

Color Doppler ultrasonographic(US) features of 28 patients with Hashimato's thyroiditis were evaluated with regard to echo and color-flow patterns. Correlation of color-flow pattern with thyroid function was performed. All 28 patients showed varying degrees of diffuse enlargement of the thyroid gland and a heterogeneous echo pattern.Color-flow pattern of increased blood flow. Low to moderate, focally increased blood flow was seen in 26 patients(92.8%). Of these 26 patients, 24 patients showed subclinical hypothyroidism or euthyroidism. Two patients who showed hyperthyroidism showed several pieces of focally increased color flow, Which was noted during both systole and diastole. Diffuse, multifocal color-flow throughout thyroid gland was seen in two patients with Hashimato's thyroiditis: one with clinical hypothyroidism and the other with subclinical hypothyroidism. Even though Hashimoto's thyroiditis showed variable color-flow patterns, we believe that heterogenous parenchymal echopattern with low or moderately increased flow is a rather characteristic feature of Hashimoto's thyroiditis, and we suggest that color Doppler US provides additional information for evaluation of Hashimoto's thyroiditis

Dispersion compensation of fiber optic communication system with direct detection using artificial neural networks (ANNs)

Science.gov (United States)

Maghrabi, Mahmoud M. T.; Kumar, Shiva; Bakr, Mohamed H.

2018-02-01

This work introduces a powerful digital nonlinear feed-forward equalizer (NFFE), exploiting multilayer artificial neural network (ANN). It mitigates impairments of optical communication systems arising due to the nonlinearity introduced by direct photo-detection. In a direct detection system, the detection process is nonlinear due to the fact that the photo-current is proportional to the absolute square of the electric field intensity. The proposed equalizer provides the most efficient computational cost with high equalization performance. Its performance is comparable to the benchmark compensation performance achieved by maximum-likelihood sequence estimator. The equalizer trains an ANN to act as a nonlinear filter whose impulse response removes the intersymbol interference (ISI) distortions of the optical channel. Owing to the proposed extensive training of the equalizer, it achieves the ultimate performance limit of any feed-forward equalizer (FFE). The performance and efficiency of the equalizer is investigated by applying it to various practical short-reach fiber optic communication system scenarios. These scenarios are extracted from practical metro/media access networks and data center applications. The obtained results show that the ANN-NFFE compensates for the received BER degradation and significantly increases the tolerance to the chromatic dispersion distortion.

Ultrasonographic findings of Achilles tendon and plantar fascia in patients with calcium pyrophosphate deposition disease.

Science.gov (United States)

Ellabban, Abdou S; Kamel, Shereen R; Abo Omar, Hanaa A S; El-Sherif, Ashraf M H; Abdel-Magied, Rasha A

2012-04-01

The aims of the study were to detect the frequency of involvement of the Achilles tendon and plantar fascia in patients with calcium pyrophosphate deposition disease (CPPD) by high-frequency gray-scale ultrasonography (US) and power Doppler sonography (PDS) and to correlate these findings with demographic and clinical data. Two groups of patients were enrolled: group I (38 patients with CPPD) and group II (22 patients with knee OA). US/PDS examination of the heels was performed to both groups. In the CPPD group, US/PDS examination of the Achilles tendon revealed: calcification in 57.9%, enthesophytosis in 57.9%, enthesopathy in 23.7%, vascular sign in 21%, bursitis in 13.2%, and cortical bone irregularity in 10.5%. US/PDS examination of plantar fascia in the CPPD group revealed: calcification in 15.8%, cortical bone irregularity in 78.9%, enthesophytosis in 60.5%, and planter fasciitis in 42.1%. In patients with CPPD, age was significantly correlated with enthesophytosis and deep retrocalcaneal bursitis (p = 0.01 and p = 0.04, respectively). Heel tenderness and posterior talalgia were significantly correlated with Achilles tendon enthesopathy, vascular sign, and deep retrocalcaneal bursitis (p = 0.0001 for each). Inferior talalgia was significantly correlated with plantar fasciitis (p = 0.0001). The sensitivity of ultrasonography for detection of calcifications in Achilles tendon and plantar fascia was 57.9% and 15.8%, respectively, and the specificity was 100% for both. To conclude, ultrasonographic Achilles tendon and plantar fascia calcifications are frequent findings in patients with CPPD. These calcifications have a high specificity and can be used as a useful indirect sign of CPPD.

Neural correlates of change detection and change blindness in a working memory task.

Science.gov (United States)

Pessoa, Luiz; Ungerleider, Leslie G

2004-05-01

Detecting changes in an ever-changing environment is highly advantageous, and this ability may be critical for survival. In the present study, we investigated the neural substrates of change detection in the context of a visual working memory task. Subjects maintained a sample visual stimulus in short-term memory for 6 s, and were asked to indicate whether a subsequent, test stimulus matched or did not match the original sample. To study change detection largely uncontaminated by attentional state, we compared correct change and correct no-change trials at test. Our results revealed that correctly detecting a change was associated with activation of a network comprising parietal and frontal brain regions, as well as activation of the pulvinar, cerebellum, and inferior temporal gyrus. Moreover, incorrectly reporting a change when none occurred led to a very similar pattern of activations. Finally, few regions were differentially activated by trials in which a change occurred but subjects failed to detect it (change blindness). Thus, brain activation was correlated with a subject's report of a change, instead of correlated with the physical change per se. We propose that frontal and parietal regions, possibly assisted by the cerebellum and the pulvinar, might be involved in controlling the deployment of attention to the location of a change, thereby allowing further processing of the visual stimulus. Visual processing areas, such as the inferior temporal gyrus, may be the recipients of top-down feedback from fronto-parietal regions that control the reactive deployment of attention, and thus exhibit increased activation when a change is reported (irrespective of whether it occurred or not). Whereas reporting that a change occurred, be it correctly or incorrectly, was associated with strong activation in fronto-parietal sites, change blindness appears to involve very limited territories.

Knowledge-guided golf course detection using a convolutional neural network fine-tuned on temporally augmented data

Science.gov (United States)

Chen, Jingbo; Wang, Chengyi; Yue, Anzhi; Chen, Jiansheng; He, Dongxu; Zhang, Xiuyan

2017-10-01

The tremendous success of deep learning models such as convolutional neural networks (CNNs) in computer vision provides a method for similar problems in the field of remote sensing. Although research on repurposing pretrained CNN to remote sensing tasks is emerging, the scarcity of labeled samples and the complexity of remote sensing imagery still pose challenges. We developed a knowledge-guided golf course detection approach using a CNN fine-tuned on temporally augmented data. The proposed approach is a combination of knowledge-driven region proposal, data-driven detection based on CNN, and knowledge-driven postprocessing. To confront data complexity, knowledge-derived cooccurrence, composition, and area-based rules are applied sequentially to propose candidate golf regions. To confront sample scarcity, we employed data augmentation in the temporal domain, which extracts samples from multitemporal images. The augmented samples were then used to fine-tune a pretrained CNN for golf detection. Finally, commission error was further suppressed by postprocessing. Experiments conducted on GF-1 imagery prove the effectiveness of the proposed approach.

Abstract computation in schizophrenia detection through artificial neural network based systems.

Science.gov (United States)

Cardoso, L; Marins, F; Magalhães, R; Marins, N; Oliveira, T; Vicente, H; Abelha, A; Machado, J; Neves, J

2015-01-01

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

The ultrasonographic diagnosis of fetal encephalocele at 13th gestational week

Directory of Open Access Journals (Sweden)

Šorak Marija

2010-01-01

Full Text Available Background. Encephalocele presents a rare anomaly of central nervous system, developed as a consequence of neural tube closing defect during early embrional development, and it is described by a baggy formation which prolaborates through the pores of the scull, filled with brain tissue, cerebrospinal liquor and entwined with meninges. According to literature search, until this day, the earliest it can be ultrasonically detected is the 13th gestation week, with the appliance of three-dimensional ultrasound. Case report. We presented 25 years old patient, ultrasonically diagnosed with occipital fetal encephalocela at the 13th gestation week. A gestation sack was located in the right uteral corn of the two-corned uterus with one cervix. The diagnosis was confirmed also by trippled value of alpha-fetoprotein in maternal serum: 75,98 IU/mL. Conclusion. Ultrasonic examination is the method of choice for prenatal detection of a fetal anomaly. It is possible to diagnose encephalocele if it prominates above the limits of the scull.

Ultrasonography of pleural effusion. The quantification of minimal detectable volume

International Nuclear Information System (INIS)

Sustic, A.; Medved, I.; Ekl, D.; Simic, O.; Kovac, D.; Ivanis, N.

2001-01-01

Background. The aim of this study was to establish a minimal volume of free thoracic fluid in the pleural space of the supine cadaver detectable by ultrasonography. Material and methods. A prospective study with an experimental model on 20 cadavers (10 male, 10 female; age 66 ±11 yr.; height 172 ±9 cm; weight 75 ±12.6 kg; body surface area (BSA) 1.87 ±0.2 m 2 ) was used. Each cadaver was punctured bilaterally in 5 th or 6 th intercostal space at the medioclavicular line with venous cannula infusing in NaCl 0,9% solution at randomised speed in the chest. During the procedure the laterodorsal part of the thoracic wall next to the pulmonal base and phrenicocostal sinus was ultrasonographically scanned. At the moment of the visualisation of anechogenic line pertaining to the free fluid between dorsal thoracic wall and lungs, the installation was stopped and the amount of injected fluid verified. Results. Minimal, by ultrasonography detectable amount of free fluid in the right pleural space was 223±52 ml with the significant positive correlation to height (r = 0.69; p < 0.001), weight (r 0.68; p < 0.01) and the BSA (r = 0.71; p < 0.001) of cadaver. Detectable volume in the left pleural space was notably smaller than contra lateral, namely 172±53 ml also with a significant correlation to the cadaver's height (r = 0.55; p < 0.05), weight (r = 0.59; p < 0.01) and BSA (r = 0.60; p < 0.01). Conclusions. The authors affirm that ultrasonographically detectable quantity of free fluid in the chest positively correlates with height, weight and BSA of cadavers, and that the measured amount in the supine position is approximately 223 ml for the right space versus 172 ml for the left pleural space. (author)

Systolic blood pressure, routine kidney variables and renal ultrasonographic findings in cats naturally infected with feline immunodeficiency virus.

Science.gov (United States)

Taffin, Elien Rl; Paepe, Dominique; Ghys, Liesbeth Fe; De Roover, Katrien; Van de Maele, Isabel; Saunders, Jimmy H; Duchateau, Luc; Daminet, Sylvie

2017-06-01

Objectives Hypertension is a common cause of proteinuria in HIV-infected people. In cats, feline immunodeficiency virus (FIV) infection appears to be associated with proteinuria. Therefore, the results from systolic blood pressure (SBP) measurements in naturally infected FIV-positive cats were reviewed to assess whether hypertension contributes to the observed proteinuria in these cats. Ultrasonographic findings in FIV-positive cats were reviewed to complete renal assessment and to extend the scant knowledge on renal ultrasonography in cats. Methods Data from client-owned, naturally infected FIV-positive cats were retrospectively reviewed. To obtain a control group, records were reviewed from age-matched, privately owned, FIV-negative cats. Results Data from 91 FIV-infected and 113 control cats were compared. FIV-infected cats showed a significantly lower SBP ( P 0.4) occurred more frequently in FIV-infected cats ( P <0.001). Renal ultrasonography showed abnormalities in 60/91 FIV-infected cats, with hyperechogenic cortices in 39/91 and enlarged kidneys in 31/91. Conclusions and relevance Hypertension can be excluded as a common cause of renal damage leading to proteinuria in FIV-infected cats. Proteinuria and poorly concentrated urine are common in naturally infected FIV-positive cats, in contrast to azotaemia. Clinicians should cautiously interpret ultrasonographic abnormalities as these occur in over half of FIV-infected cats.

Detecting and diagnosing SSME faults using an autoassociative neural network topology

Science.gov (United States)

Ali, M.; Dietz, W. E.; Kiech, E. L.

1989-01-01

An effort is underway at the University of Tennessee Space Institute to develop diagnostic expert system methodologies based on the analysis of patterns of behavior of physical mechanisms. In this approach, fault diagnosis is conceptualized as the mapping or association of patterns of sensor data to patterns representing fault conditions. Neural networks are being investigated as a means of storing and retrieving fault scenarios. Neural networks offer several powerful features in fault diagnosis, including (1) general pattern matching capabilities, (2) resistance to noisy input data, (3) the ability to be trained by example, and (4) the potential for implementation on parallel computer architectures. This paper presents (1) an autoassociative neural network topology, i.e. the network input and output is identical when properly trained, and hence learning is unsupervised; (2) the training regimen used; and (3) the response of the system to inputs representing both previously observed and unkown fault scenarios. The effects of noise on the integrity of the diagnosis are also evaluated.

Ultrasonographic Findings of Subcutaneous and Muscular Sparganosis

International Nuclear Information System (INIS)

Park, Hee Jin; Park, Noh Hyuck; Lee, Eun Ja; Park, Chan Sub; Lee, Sung Moon; Park, Sung Il

2009-01-01

This study was deigned to evaluate the ultrasonographic findings of subcutaneous and intramuscular sparganosis. Nine cases of histologically proven subcutaneous and intramuscular sparganosis lesions in seven patients (mean patient age, 59 years; M:F = 6:1) were reviewed retrospectively. Two patients had recurrent sparganosis. A color Doppler examination was performed in all cases. A prior history of ingestion of raw snake meat was noted for two patients. Patients presented with a palpable mass and induration (n = 7) and dull pain (n = 4). Lesion locations were in the thigh (n = 4), lower leg (n = 2), chest wall (n = 1), an inguinal location (n = 1) and the neck (n = 1). Five lesions were in the subcutaneous fat layer and four lesions had intramuscular locations. Calcification was noted in two cases. All cases showed heterogeneous hypoechoic serpiginous tubular-and-oval lesions. The lesions were conglomerated or discrete in appearance. All nine cases showed the presence of lesions with a multi-layered wall with variable intraluminal echogenicity, at least in one segment of the lesion. Increased vascularity was noted on color Doppler examinations in two patients with pain. Subcutaneous or intramuscular sparganosis should be included in the differential diagnosis when a serpiginous tubular-and-oval lesion is noted that is seen with a multi-layered wall with variable intraluminal echogenicity

Neural Network Based Sensory Fusion for Landmark Detection