telehealth and computer-aided diagnosis

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computer-aided diagnosis for breast cancer telehealth and computer-aided diagnosis 1 telehealth telehealth is the delivery of health-related services and information via telecommunications technologies. telehealth could be as simple as two health professionals discussing a case over the telephone or as complicated as doing robotic surgery between facilities at different ends of the globe. 2 telehealth and computer-aided diagnosis telehealth is an interdiscipline that involes medical image processing, networking, cybersecurity, machine learning, database management, and data mining. computer-aided diagnosis (cad) systems use computer techniques to assist doctors to analyze data and make diagnosis. computer-aided diagnosis (cad) systems could be able to serve independently as one terminal in the telehealth network in the future. 3 cad for breast cancer breast cancer is the #1 leading cause of cancer death for women at ages 20 to 59 [1,2]. 226,870 newly diagnosed cases and 39,510 deaths in the united states in 2012 [2]. the …

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and portable. more sensitive than mammography for dense breasts ( i.e., suitable for women younger than 35 years old[12]). 6 computer-aided diagnosis (cad) advantages of a cad system: calculate computational features and statistical features, which cannot be obtained visually or intuitively by humans (doctors). minimize the operator-dependent nature of ultrasound imaging [4] and make the diagnosis process reproducible. 7 computer-aided diagnosis (cad) preprocessing: enhance the image contrast and reduce speckle noise. segmentation: separate the lesion from the surrounding tissues. feature extraction: extract critical features to distinguish benign and malignant lesions. classification: use machine learning techniques to classify the lesion into benign or malignant types. 8 computer-aided diagnosis (cad) segmentation is the most important step. since many crucial features to distinguish benign and malignant lesions are based on the shape and boundary of the lesion. 9 example of benign and malignant lesions malignant benign the shape and margin can only be …

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er region deletion roi generation 15 rank the regions. the one with the highest score is considered as the lesion region. (e) image of the winning region roi generation 16 roi generation result (a) original bus image (b) binary image (c) winning region (d) roi generation 17 the proposed method cont. roi generation speckle reduction [5] contrast enhancement neutrosophic l-means 18 speckle reduction an effective and fast algorithm is used: speckle reducing anisotropic diffusion (srad) [5] 19 the proposed method cont. roi generation speckle reduction [5] contrast enhancement neutrosophic l-means 20 fourier transform spatial domain  frequency domain 1-dimentional characterize different structures 21 from 1-d signal to 2-d image 2-d log-gabor filters defined in polar coordinates: where k is related to the bandwidth of the filter and w0 is the center frequency of the filter. q0 is the orientation. sq defines the spread of the gaussian orientation function. 6 orientations …

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malignant. every lesion is manually outlined by an experienced radiologist. the manual delineations are served as the standard to evaluate the segmentation method. 30 comparison is necessary compare the new method with: a segmentation method using active contour model [21] a segmentation method using level-set model [28] a segmentation method using watershed model [70] 31 result of segmentation methods (a) the original image. (b) manual delineation by radiologist. (c) output of the method in [21]. (d) output of the method in [28]. (e) output of the method in [70]. (f) output of the proposed method. 32 area error metrics 33 accuracy comparison 34 future direction continue the research on breast cancer ultrasound, to find more reliable segmentation methods, to extract the features of tumors, and to train classifiers that can automatically classify tumors into benign/malignant. dental x-rays dataset, to detect tooth root and dental diseases. other type of medical images …

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.t. speckle reducing anisotropic diffusion. ieee trans. on image processing 11, 11 (2002), 1260-1270. 6.shankar, p.m., piccoli, c.w., reid, j.m., forsberg, f., and goldberg, b.b. application of the compound probability density function for characterization of breast masses in ultrasound b scans. physics in medicine & biology 50, 10 (2005), 2241-2248. 7.taylor, k.j.w., merritt, c., piccoli, c., schmidt, r., rouse, g., fornage, b., rubin, e., georgian-smith, d., winsberg, f., goldberg, b., and mendelson, e. ultrasound as a complement to mammography and breast examination to characterize breast masses. ultrasound in medicine & biology 28, 1 (2002), 19-26. 8.zhi, h., ou, b., luo, b.-m., feng, x., wen, y.-l., and yang, h.-y. comparison of ultrasound elastography, mammography, and sonography in the diagnosis of solid breast lesions. j. ultrasound in medicine 26, 6 (2007), 807-815. 9.chang, r.-f., wu, w.-j., moon, w.k., and chen, d.-r. improvement in breast tumor discrimination by support vector machines and speckle-emphasis …

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computer-aided diagnosis for breast cancer telehealth and computer-aided diagnosis 1 telehealth telehealth is the delivery of health-related services and information via telecommunications technologies. telehealth could be as simple as two health professionals discussing a case over the telephone or as complicated as doing robotic surgery between facilities at different ends of the globe. 2 telehealth and computer-aided diagnosis telehealth is an interdiscipline that involes medical image processing, networking, cybersecurity, machine learning, database management, and data mining. computer-aided diagnosis (cad) systems use computer techniques to assist doctors to analyze data and make diagnosis. computer-aided diagnosis (cad) systems could be able to serve independently as one terminal in...

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