Computer-aided Diagnosis Scheme For Detection Research Articles

A Computer-Aided Diagnosis Scheme For Detection Of Fatty Liver In Vivo Based On Ultrasound Kurtosis Imaging.

Fatty liver disease is a common disease caused by alcoholism, obesity, and diabetes, resulting in triglyceride accumulation in hepatocytes. Kurtosis coefficient, a measure of the peakedness of the probability distribution, has been applied to the analysis of backscattered statistics for characterizing fatty liver. This study proposed ultrasound kurtosis imaging as a computer-aided diagnosis (CAD) method to visually and quantitatively stage the fatty liver. A total of 107 patients were recruited to participate in the experiments. The livers were scanned using a clinical ultrasound scanner with a 3.5-MHz curved transducer to acquire the raw ultrasound backscattered signals for kurtosis imaging. The kurtosis image was constructed using the sliding window technique. Experimental results showed that kurtosis imaging has the ability to visualize and quantify the variation of backscattered statistics caused by fatty infiltration. The kurtosis coefficient corresponding to liver parenchyma decreased from 5.41 ± 0.89 to 3.68 ± 0.12 with increasing the score of fatty liver from 0 (normal) to 3 (severe), indicating that fatty liver reduces the degree of peakedness of backscattered statistics. The best performance of kurtosis imaging was found when discriminating between normal and fatty livers with scores ≥1: the area under the curve (AUC) is 0.92 at a cutoff value of 4.36 (diagnostic accuracy =86.9 %, sensitivity =86.7 %, specificity =87.0 %). The current findings suggest that kurtosis imaging may be useful in designing CAD tools to assist in physicians in early detection of fatty liver.

Computer-aided Diagnosis Scheme for Detection of Mass with Bilateral Subtraction Technique in Mamograms

Computer-aided Diagnosis Scheme for Detection of Mass with Bilateral Subtraction Technique in Mamograms

Computer-Aided Diagnosis Scheme for Detection of Lacunar Infarcts on MR Images

The detection and management of asymptomatic lacunar infarcts on magnetic resonance (MR) images are important tasks for radiologists to ensure the prevention of severe cerebral infarctions. However, accurate identification of the lacunar infarcts on MR images is a difficult task for the radiologists. Therefore the purpose of this study was to develop a computer-aided diagnosis scheme for the detection of lacunar infarcts to assist radiologists' interpretation as a "second opinion." Our database comprised 1,143 T1- and 1,143 T2-weighted images obtained from 132 patients. The locations of the lacunar infarcts were determined by experienced neuroradiologists. We first segmented the cerebral region in a T1-weighted image by using a region growing technique for restricting the search area of lacunar infarcts. For identifying the initial lacunar infarcts candidates, a top-hat transform and multiple-phase binarization were then applied to the T2-weighted image within the segmented cerebral region. For eliminating the false positives (FPs), we determined 12 features--the locations x and y, signal intensity differences in the T1- and T2-weighted images, nodular components from a scale of 1 to 4, and nodular and linear components from a scale of 1 to 4. The nodular components and the linear components were obtained using a filter bank technique. The rule-based schemes and a support vector machine with 12 features were applied to the regions of the initial candidates for distinguishing between lacunar infarcts and FPs. Our computerized scheme was evaluated by using a holdout method. The sensitivity of the detection of lacunar infarcts was 96.8% (90/93) with 0.76 FP per image. Our computerized scheme would be useful in assisting radiologists for identifying lacunar infarcts in MR images.

Standards for Gastroenterologists for Performing and Interpreting Diagnostic Computed Tomographic Colonography

Standards for Gastroenterologists for Performing and Interpreting Diagnostic Computed Tomographic Colonography

Detection of clustered microcalcifications on mammograms using new filter bank

We have developed a computer-aided diagnosis scheme for detection of clustered microcalcifications at the early stage in mammograms. In the proposed method, the mammogram image is input to a filter bank for detecting nodular/line patterns. Then, the nodular pattern-enhanced image, the nodular/line pattern-enhanced image, and the second-order difference images for horizontal, vertical, and diagonal directions are generated at each resolution level. Next, the mammogram image is partitioned into regions of interest (5 mm × 5 mm). For each region of interest, based on the images generated from the filter bank at each resolution level, the objective features are extracted and used to discriminate the region of interest containing the clustered microcalcifications and the region of interest for normal tissue. Finally, discriminant analysis using these objective features is employed for detecting the clustered microcalcifications. A detection experiment was performed using 59 mammograms containing 106 clustered microcalcifications at the early stage; the sensitivity was 94.3% (100/106 clustered microcalcifications), the number of false positives was 0.051 per image, indicating that the proposed method is useful in detecting clustered microcalcifications at the early stage. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(5): 68–79, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.20172

Improvement of Computer-aided Diagnosis Scheme for Detection of Clustered Microcalcifications in Mammograms:

Improvement of Computer-aided Diagnosis Scheme for Detection of Clustered Microcalcifications in Mammograms:

Computer-aided diagnosis scheme for detection of polyps at CT colonography.

Colon cancer is one of the leading causes of cancer deaths in the United States. However, most colon cancers can be prevented if precursor colonic polyps are detected and removed. An advanced computer-aided diagnosis (CAD) scheme was developed for the automated detection of polyps at computed tomographic (CT) colonography. A region encompassing the colonic wall is extracted from an isotropic volume data set obtained by interpolating CT colonographic scans along the axial direction. Polyp candidates are detected with computation of three-dimensional (3D) geometric features that characterize polyps, followed by extraction of polyps with hysteresis thresholding and fuzzy clustering using these geometric features. The number of false-positive findings is reduced by extracting 3D texture features from polyp candidates and applying quadratic discriminant analysis to the candidates. This CAD scheme was applied in 71 patients who underwent CT colonography, 14 of whom had colonoscopically confirmed polyps (n = 21). At by-patient analysis, sensitivity was 100%, with an average false-positive rate of 2.0 per patient. At by-polyp analysis, the scheme detected 90% of the polyps at the same false-positive rate. This CAD scheme permits accurate detection of suspicious lesions and thus has the potential to reduce radiologists' interpretation time and improve their diagnostic accuracy in the detection of polyps at CT colonography.

Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps

We have developed a three-dimensional (3-D) computer-aided diagnosis scheme for automated detection of colonic polyps in computed tomography (CT) colonographic data sets, and assessed its performance based on colonoscopy as the gold standard. In this scheme, a thick region encompassing the entire colonic wall is extracted from an isotropic volume reconstructed from the CT images in CT colonography. Polyp candidates are detected by first computing of 3-D geometric features that characterize polyps, folds, and colonic walls at each voxel in the extracted colon, and then segmenting of connected components corresponding to suspicious regions by hysteresis thresholding based on these geometric features. We apply fuzzy clustering to these connected components to obtain the polyp candidates. False-positive (FP) detections are then reduced by computation of several 3-D volumetric features characterizing the internal structures of the polyp candidates, followed by the application of discriminant analysis to the feature space generated by these volumetric features. The locations of the polyps detected by our computerized method were compared to the gold standard of conventional colonoscopy. The performance was evaluated based on 43 clinical cases, including 12 polyps determined by colonoscopy. Our computerized scheme was shown to have the potential to detect polyps in CT colonography with a clinically acceptable high sensitivity and a low FP rate.

A genetic algorithm-based method for optimizing the performance of a computer-aided diagnosis scheme for detection of clustered microcalcifications in mammograms.

Computer-aided diagnosis (CAD) schemes have the potential of substantially increasing diagnostic accuracy in mammography by providing the advantages of having a second reader. Our laboratory has developed a CAD scheme for detecting clustered microcalcifications in digital mammograms that is being tested clinically at the University of Chicago Hospitals. Our CAD scheme contains a large number of parameters such as filter weights, threshold levels, and region of interest (ROI) sizes. The choice of these parameter values determines the overall performance of the system and thus must be carefully set. Unfortunately, when the number of parameters becomes large, it is very difficult to obtain the optimal performance, especially when the values of the parameters are correlated with each other. In this study, we address the problem of identifying the optimal overall performance by developing an automated method for the determination of the parameter values that maximize the performance of a mammographic CAD scheme. Our method utilizes a genetic algorithm to search through the possible parameter values, and provides the set of parameters that minimize a cost function which measures the performance of the scheme. Using a database of 89 digitized mammograms, our method demonstrated that the sensitivity of our CAD scheme can be increased from 80% to 87% at a false positive rate of 1.0 per image. We estimate the average performance of our CAD scheme on unknown cases by performing jackknife tests; this was previously not feasible when the parameters of the CAD scheme were determined in a nonautomated manner.