Proposed Computer-aided Diagnosis System Research Articles

A New CAD System for Breast Microcalcifications Diagnosis

Breast cancer is one of the most deadly cancers in the world, especially among women. With no identified causes and absence of effective treatment, early detection remains necessary to limit the damages and provide possible cure. Submitting women with family antecedent to mammography periodically can provide an early diagnosis of breast tumors. Computer Aided Diagnosis (CAD) is a powerful tool that can help radiologists improving their diagnostic accuracy at earlier stages. Several works have been developed in order to analyze digital mammographies, detect possible lesions (especially masses and microcalcifications) and evaluate their malignancy. In this paper a new approach of breast microcalcifications diagnosis on digital mammograms is introduced. The proposed approach begins with a preprocessing procedure aiming artifacts and pectoral muscle removal based on morphologic operators and contrast enhancement based on galactophorous tree interpolation. The second step of the proposed CAD system consists on segmenting microcalcifications clusters, using Generalized Gaussian Density (GGD) estimation and a Bayesian back-propagation neural network. The last step is microcalcifications characterization using morphologic features which are used to feed a neuro-fuzzy system to classify the detected breast microcalcifications into benign and malignant classes.

An Automated Computer-aided Diagnosis System for Abdominal CT Liver Images

In this paper, we present a computer-aided diagnosis (CAD) system for abdominal Computed Tomography liver images that comprises four main phases: liver segmentation, lesion candidate segmentation, feature extraction from each candidate lesion, and liver disease classification. A hybrid approach based on fuzzy clustering and grey wolf optimisation is employed for automatic liver segmentation. Fast fuzzy c-means clustering is used for lesion candidates extraction, and a variety of features are extracted from each candidate. Finally, these features are used in a classification stage using a support vector machine. Experimental results confirm the efficacy of the proposed CAD system, which is shown to yield an overall accuracy of almost 96% in terms of healthy liver extraction and 97% for liver disease classification.

Computer Aided Diagnosis System for Early Lung Cancer Detection

Lung cancer continues to rank as the leading cause of cancer deaths worldwide. One of the most promising techniques for early detection of cancerous cells relies on sputum cell analysis. This was the motivation behind the design and the development of a new computer aided diagnosis (CAD) system for early detection of lung cancer based on the analysis of sputum color images. The proposed CAD system encompasses four main processing steps. First is the preprocessing step which utilizes a Bayesian classification method using histogram analysis. Then, in the second step, mean shift segmentation is applied to segment the nuclei from the cytoplasm. The third step is the feature analysis. In this step, geometric and chromatic features are extracted from the nucleus region. These features are used in the diagnostic process of the sputum images. Finally, the diagnosis is completed using an artificial neural network and support vector machine (SVM) for classifying the cells into benign or malignant. The performance of the system was analyzed based on different criteria such as sensitivity, specificity and accuracy. The evaluation was carried out using Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate the efficiency of the SVM classifier over other classifiers, with 97% sensitivity and accuracy as well as a significant reduction in the number of false positive and false negative rates.

전산화단층촬영 영상을 이용한 뇌출혈 질감특징분석

In this study we proposed a texture feature analysis algorithm that distinguishes between a normal image and a diseased image using CT images of some brain hemorrhage patients, and generates both Eigen images and test images which can be applied to the proposed computer aided diagnosis system in order to perform a quantitative analysis for 6 parameters. And through the analysis, we derived and evaluated the recognition rate of CT images of brain hemorrhage. As the results of examining over 40 example CT images of brain hemorrhage, the recognition rates representing a specific texture feature-value are as follows: some appeared to be as high as 100% including average gray level, average contrast, smoothness, and Skewness while others showed a little low disease recognition rate: 95% for uniformity and 87.5% for entropy. Consequently, based on this research result, if a software that enables a computer aided diagnosis system for medical images is developed, it will lead to the availability for the automatic detection of a diseased spot in CT images of brain hemorrhage and quantitative analysis. And they can be used as computer aided diagnosis data, resulting in the increased accuracy and the shortened time in the stage of final reading.

Computer-Aided Diagnosis System for Alzheimer's Disease Using Different Discrete Transform Techniques.

The different discrete transform techniques such as discrete cosine transform (DCT), discrete sine transform (DST), discrete wavelet transform (DWT), and mel-scale frequency cepstral coefficients (MFCCs) are powerful feature extraction techniques. This article presents a proposed computer-aided diagnosis (CAD) system for extracting the most effective and significant features of Alzheimer's disease (AD) using these different discrete transform techniques and MFCC techniques. Linear support vector machine has been used as a classifier in this article. Experimental results conclude that the proposed CAD system using MFCC technique for AD recognition has a great improvement for the system performance with small number of significant extracted features, as compared with the CAD system based on DCT, DST, DWT, and the hybrid combination methods of the different transform techniques.

Breast mass classification in digital mammography based on extreme learning machine

This paper presents a novel computer-aided diagnosis (CAD) system for the diagnosis of breast cancer based on extreme learning machine (ELM). In view of a mammographic image, it is first eliminated interference in the preprocessing stages. Then, the preprocessed images are segmented by the level set model we proposed. Subsequently, a model of multidimensional feature vectors is built. Since not every feature vector contributes to the improvement of performance, feature selection is done by the combination of support vector machine (SVM) and extreme learning machine (ELM). Finally, an optimal subset of feature vectors is inputted into the classifiers for distinguishing malignant masses from benign ones. We also compare our breast mass classification approach based on ELM with several state-of-the-art classification models, and the results show that the proposed CAD system not only has good performance in terms of specificity, sensitivity and accuracy, but also achieves a significant reduction in training time compared with SVM and particle swarm optimization-support vector machine (PSO-SVM). Ultimately, our system achieves the better performance with average accuracy of 96.02% which indicates that the proposed segmentation model, the utilization of selected feature vectors and the effective classifier ELM provide satisfactory system.

Quantitative breast lesion classification based on multichannel distributions in shear-wave imaging

Background and objectivesA computer-aided diagnosis (CAD) system based on the quantified color distributions in shear-wave elastography (SWE) was developed to evaluate the malignancies of breast tumors. MethodsFor 57 benign and 31 malignant tumors, 18 SWE features were extracted from regions of interest (ROI), including the tumor and peritumoral areas. In the ROI, a histogram in each color channel was described using moments such as the mean, variance, skewness, and kurtosis. Moreover, three color channels were combined as a vector to evaluate tissue elasticity. The SWE features were then combined in a logistic regression classifier for breast tumor classification. ResultsThe performance of the CAD system achieved an accuracy of 81%. Combining the CAD system with a BI-RADS assessment obtained an Az improvement from 0.77 to 0.89 (p-value <0.05). ConclusionsThe combination of the proposed CAD system based on SWE features and the BI-RADS assessment would provide a promising diagnostic suggestion.

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images.

A rapid and highly accurate diagnostic tool for distinguishing benign tumors from malignant ones is required owing to the high incidence of breast cancer. Although various computer-aided diagnosis (CAD) systems have been developed to interpret ultrasound images of breast tumors, feature selection and the setting of parameters are still essential to classification accuracy and the minimization of computational complexity. This work develops a highly accurate CAD system that is based on a support vector machine (SVM) and the artificial immune system (AIS) algorithm for evaluating breast tumors. Experiments demonstrate that the accuracy of the proposed CAD system for classifying breast tumors is 96.67%. The sensitivity, specificity, PPV, and NPV of the proposed CAD system are 96.67, 96.67, 95.60, and 97.48%, respectively. The receiver operator characteristic (ROC) area index A z is 0.9827. Hence, the proposed CAD system can reduce the number of biopsies and yield useful results that assist physicians in diagnosing breast tumors.

A Computer-Aided Diagnosis System for Dynamic Contrast-Enhanced MR Images Based on Level Set Segmentation and ReliefF Feature Selection

This study established a fully automated computer-aided diagnosis (CAD) system for the classification of malignant and benign masses via breast magnetic resonance imaging (BMRI). A breast segmentation method consisting of a preprocessing step to identify the air-breast interfacing boundary and curve fitting for chest wall line (CWL) segmentation was included in the proposed CAD system. The Chan-Vese (CV) model level set (LS) segmentation method was adopted to segment breast mass and demonstrated sufficiently good segmentation performance. The support vector machine (SVM) classifier with ReliefF feature selection was used to merge the extracted morphological and texture features into a classification score. The accuracy, sensitivity, and specificity measurements for the leave-half-case-out resampling method were 92.3%, 98.2%, and 76.2%, respectively. For the leave-one-case-out resampling method, the measurements were 90.0%, 98.7%, and 73.8%, respectively.

Computer-aided assessment of tumor grade for breast cancer in ultrasound images.

This study involved developing a computer-aided diagnosis (CAD) system for discriminating the grades of breast cancer tumors in ultrasound (US) images. Histological tumor grades of breast cancer lesions are standard prognostic indicators. Tumor grade information enables physicians to determine appropriate treatments for their patients. US imaging is a noninvasive approach to breast cancer examination. In this study, 148 3-dimensional US images of malignant breast tumors were obtained. Textural, morphological, ellipsoid fitting, and posterior acoustic features were quantified to characterize the tumor masses. A support vector machine was developed to classify breast tumor grades as either low or high. The proposed CAD system achieved an accuracy of 85.14% (126/148), a sensitivity of 79.31% (23/29), a specificity of 86.55% (103/119), and an A Z of 0.7940.

Application of a Computer-aid Diagnosis of Pneumoconiosis for CR X-ray Images

This paper presents a method for applying a computer-aided diagnosis (CAD) for pneumoconiosis to chest X-ray images digitalized by the computed radiography (CR) system . When we reported the CAD before, we showed performance of the CAD for chest X-ray images digitalized by a CCD scanner. However, since density distribution of the CR X-ray images is quite different from the images digitalized by CCD scanner, it is necessary to equip some preprocessing into the CAD for applying to the CR images. In this paper, as the first trial for the application, drawing rib edges and additional lines on the CR images by a tablet PC, we examined whether the CAD can be interactively applied to the CR images. U sing 51 chest CR X-ray images, we compared the proposed CAD system with the existing system for the images digitalized by CCD scanner.

Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM.

This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant. Original mammogram is preprocessed to separate the breast region from its background. To work on the suspicious area of the breast, region of interest (ROI) patches of a fixed size of 128×128 are extracted from the original large-sized digital mammograms. For training, patches are extracted manually from a preprocessed mammogram. For testing, patches are extracted from a highly dense area identified by clustering technique. For all extracted patches corresponding to a mammogram, Zernike moments of different orders are computed and stored as a feature vector. A support vector machine (SVM) is used to classify extracted ROI patches. The experimental study shows that the use of Zernike moments with order 20 and SVM classifier gives better results among other studies. The proposed system is tested on Image Retrieval In Medical Application (IRMA) reference dataset and Digital Database for Screening Mammography (DDSM) mammogram database. On IRMA reference dataset, it attains 99% sensitivity and 99% specificity, and on DDSM mammogram database, it obtained 97% sensitivity and 96% specificity. To verify the applicability of Zernike moments as a fitting texture descriptor, the performance of the proposed CAD system is compared with the other well-known texture descriptors namely gray-level co-occurrence matrix (GLCM) and discrete cosine transform (DCT).

Computer Aided Diagnosis System Feature Extraction of Alzheimer Disease Using MFCC

Mel-Scale Frequency Cepstral Coefficients (MFCC) is very efficient technique for feature extraction. This paper proposes a Computer Aided Diagnosis (CAD) system for extracting the most effective and significant features of Alzheimer Disease (AD) using MFCC technique for the 3-D MRI images. Classification is performed using Linear Support Vector Machine (SVM). Experimental results represent that the proposed CAD system using MFCC for AD recognition give excellent accuracy with small number of significant extracted features which reduces the memory size and simplify the hardware implementation of the CAD system. The proposed approach have better performance and stability.

Computer-Aided Diagnosis Based on Quantitative Elastographic Features with Supersonic Shear Wave Imaging

Supersonic shear wave imaging (SSI) has recently been explored as a technique to evaluate tissue elasticity modulus and has become a valuable tool for tumor characterization. The purpose of this study was to develop a novel computer-aided diagnosis (CAD) system that can acquire quantitative elastographic information from color SSI elastography images automatically and objectively for the purpose of classifying benign and malignant breast tumors. Conventional ultrasonography (US) and SSI elastography images of 125 breast tumors (81 benign, 44 malignant), in 93 consecutive patients (mean age: 40 y, age range: 16–75 y), were obtained. After reconstruction of tissue elasticity data and automatic segmentation of each breast tumor, 10 quantitative elastographic features of the tumor and peri-tumoral areas, respectively (elasticity modulus mean, maximum and standard deviation, hardness degree and elasticity ratio), were computed and evaluated. A support vector machine (SVM) classifier was used for optimum classification via combination of these features. The B-mode Breast Imaging Reporting and Data System (BI-RADS) was used to compare gray-scale US and SSI elastography with respect to diagnostic performance. Histopathologic examination was used as the reference standard. Student's t-test, the Mann-Whitney U-test, the point biserial correlation coefficient and receiver operating characteristic curve analysis were performed for statistical analysis. As a result, the accuracy, sensitivity, specificity, positive predictive value and negative predictive value of benign/malignant classification were 95.2% (119/125), 90.9% (40/44), 97.5% (79/81), 95.2% (40/42) and 95.2% (79/83) for the CAD scheme, respectively, and 79.2% (99/125), 90.9% (40/44), 72.8% (59/81), 64.5% (40/62) and 93.7% (59/63) for BI-RADS assessment, respectively. The area under the receiver operating characteristic curve (Az value) for the proposed CAD system using the combination of elastographic features was significantly higher than the Az value for visual assessment by the radiologists using BI-RADS (0.97 vs. 0.91). The results indicate that SSI elastography could be used for computer-aided feature extraction, and the proposed CAD method could improve the diagnostic accuracy of classification of breast tumors to avoid unnecessary biopsy. Furthermore, elastographic features of the peri-tumoral area have the potential to provide critical information in differential diagnosis.

An Ultrasound-Based Computer-Aided Diagnosis Tool for Steatosis Detection

Liver steatosis is a common disease usually associated with social and genetic factors. Early detection and quantification is important since it can evolve to cirrhosis. In this paper, a new computer-aided diagnosis (CAD) system for steatosis classification, in a local and global basis, is presented. A Bayes factor is computed from objective ultrasound textural features extracted from the liver parenchyma. The goal is to develop a CAD screening tool to help in the steatosis detection. Results showed an accuracy of 93.33%, with a sensitivity of 94.59% and specificity of 92.11%, using the Bayes classifier. The proposed CAD system is a suitable graphical display for steatosis classification.

Mammogram Analysis Based on Pixel Intensity Mean Features

Problem statement: In the recent years, Computer Aided Diagnosis (CAD) can be very useful for detection of breast cancer. Mammography can be used as an efficient tool for breast cancer diagnosis. A computer based diagnosis and classification system can reduce unnecessary biopsy. Approach: This study investigates a new approach to the classification of mammogram images based on pixel intensity mean features. The proposed method for the classification of normal and abnormal (cancerous) pattern is a two step process. The first step is feature extraction. The intensity based features are extracted from the digital mammograms. The second step is the classification process, differentiating between normal and abnormal pattern. Artificial neural networks are used to classify the data. Experimental evaluation is performed on the Digital Database for Screening Mammography (DDSM), benchmark database. Results and Conclusion: Experiments are performed to verify that the proposed pixel intensity mean features improve the accuracy of the classification. The proposed CAD system achieves better classification performance with the accuracy of 98%.

Computer‐aided classification of breast masses using speckle features of automated breast ultrasound images

To develop an ultrasound computer-aided diagnosis (CAD) system using speckle features of automated breast ultrasound (ABUS) images. The ABUS images of 147 pathologically proven breast masses (76 benign and 71 malignant cases) were used. For each mass, a volume of interest (VOI) was cropped to define the tumor area, and the average number of speckle pixels within a VOI was calculated. In addition, first-order and second-order statistical analyses of the speckle pixels were used to quantify the information of gray-level distributions and the spatial relations among the pixels. Receiver operating characteristic curve analysis was used to evaluate the performance. The proposed CAD system based on speckle patterns achieved an accuracy of 84.4% (124∕147), a sensitivity of 83.1% (59∕71), a specificity of 85.5% (65∕76), and an Az of 0.91. The performance indices of the speckle features were comparable to the performance indices of the morphological features, which include shape and ellipse-fitting features (p-value > 0.05). Furthermore, combining speckle and morphological features yielded an Az that was significantly better than the Az of the morphological features alone (0.96 vs 0.91, p-value = 0.0154). The results suggest that the proposed speckle features, while combined with morphological features, are promising for the classification of breast masses detected using ABUS.

Computer-Aided Diagnosis Based on Speckle Patterns in Ultrasound Images

For breast ultrasound, the scatterer number density from backscattered echo was demonstrated in previous research to be a useful feature for tumor characterization. To take advantage of the scatterer number density in B-mode images, spatial compound imaging was obtained, and the statistical properties of speckle patterns were analyzed in this study for use in distinguishing between benign and malignant lesions. A total of 137 breast masses (95 benign cases and 42 malignant cases) were used in the proposed computer-aided diagnosis (CAD) system. For each mass, the average number of speckle pixels in a region of interest (ROI) was calculated to use the concept of scatterer number density. In addition, the first-order and second-order statistics of the speckle pixels were quantified to obtain the distributions of the pixel values and the spatial relations among the pixels. The performance of the speckle features extracted from each ROI was compared with the performance of the segmentation features extracted from each segmented tumor. As a result, the proposed CAD system using the speckle features achieved an accuracy of 89.1% (122/137); a sensitivity of 81.0% (34/42); and a specificity of 92.6% (88/95). All of the differences between the speckle features and the segmentation features are not statistically significant (p > 0.05). In a receiver operating characteristic (ROC) curve analysis, the Az value, area under ROC curve, of the speckle features was significantly better than the Az value of the segmentation features (0.93 vs. 0.86, p = 0.0359). The performance of this approach supports the notion that the speckle patterns induced by the scatterers in tissues can provide information for classifying tumors. The proposed speckle features, which were extracted readily from drawing an ROI without any preprocessing, also provide a more efficient classification approach than tumor segmentation.

Functional brain image classification using association rules defined over discriminant regions

This letter shows a novel computer aided diagnosis (CAD) system for the early diagnosis of Alzheimer’s Disease (AD). The proposed method evaluates the reliability of association rules (ARs) aiming to discover interesting associations between attributes in functional brain imaging, i.e. single photon emission computed tomography (SPECT) and positron emission tomography (PET). AR mining firstly requires a masking process for reducing the computational cost, which is based on Fisher discriminant ratio (FDR), in order to identify “transactions” or relationships among discriminant brain areas. Once the activation map is achieved by means of activation estimation (AE), the resulting regions of interest (ROIs) are subjected to AR discovery with a specified minimum support and confidence. Finally, the proposed CAD system performs image classification by evaluating the number of previously mined rules from controls that are verified by each subject. Several experiments were carried out on two different image modalities (SPECT and PET) in order to highlight the generalization ability of the proposed method. The AR-based method yields an accuracy up to 92.78% (with 87.5% sensitivity and 100% specificity) and 91.33% (with 82.67% sensitivity and 100% specificity) for SPECT and PET, respectively, thus outperforming recently developed methods for early diagnosis of AD.

Symptomatic vs. Asymptomatic Plaque Classification in Carotid Ultrasound

Quantitative characterization of carotid atherosclerosis and classification into symptomatic or asymptomatic type is crucial in both diagnosis and treatment planning. This paper describes a computer-aided diagnosis (CAD) system which analyzes ultrasound images and classifies them into symptomatic and asymptomatic based on the textural features. The proposed CAD system consists of three modules. The first module is preprocessing, which conditions the images for the subsequent feature extraction. The feature extraction stage uses image texture analysis to calculate Standard deviation, Entropy, Symmetry, and Run Percentage. Finally, classification is performed using AdaBoost and Support Vector Machine for automated decision making. For Adaboost, we compared the performance of five distinct configurations (Least Squares, Maximum- Likelihood, Normal Density Discriminant Function, Pocket, and Stumps) of this algorithm. For Support Vector Machine, we compared the performance using five different configurations (linear kernel, polynomial kernel configurations of different orders and radial basis function kernels). SVM with radial basis function kernel for support vector machine presented the best classification result: classification accuracy of 82.4%, sensitivity of 82.9%, and specificity of 82.1%. We feel that texture features coupled with the Support Vector Machine classifier can be used to identify the plaque tissue type. An Integrated Index, called symptomatic asymptomatic carotid index (SACI), is proposed using texture features to discriminate symptomatic and asymptomatic carotid ultrasound images using just one index or number. We hope this SACI can be used as an adjunct tool by the vascular surgeons for daily screening.