Yateen S. Chitre

Artificial neural network based classification of mammographic microcalcifications using image structure and cluster features

Breast cancer is the leading cause of death among women. Mammography is the only effective and viable technique to detect breast cancer, sometimes before the cancer becomes invasive. About 30% to 50% of breast cancers demonstrate clustered microcalcifications. We investigate the potential of using second-order histogram textural features for their correlation with malignancy. A combination of image structure features extracted from the second histogram was used with binary cluster features extracted from segmented calcifications. Several architectures of neural networks were used for analyzing the features. The neural network yielded good results for the classification of hard-to-diagnose cases of mammographic microcalcification into benign malignant categories using the selected set of features.<<ETX>>

M-band wavelet discrimination of natural textures

Abstract The M-band wavelet decomposition, a direct generalization of the standard 2-band wavelet decomposition has been applied to the problem of discriminating natural textures of varying sizes. Regular, M-band filter banks were designed using a genetic algorithm search strategy over the Householder parameter space of M-band wavelets. An exhaustive M-band decomposition was performed on 20 natural textures and energy features were extracted for each decomposed sub-band. The discrimination ability of the extracted features was compared for values of M=2, 3 and 4. A nearest neighbor algorithm was used to classify a test set of 700 images to an accuracy of 99.5%. The performance was compared with a complete decomposition and decomposition using an irregular M-band filter bank. Statistical tests were used to evaluate the average performance of features extracted from the decomposed sub-bands.

Radial-basis-function based classification of mammographic microcalcifications using texture features

Mammography has been established as the only effective and viable technique to detect breast cancer especially in the case of nonpalpable and minimal tumors. About 30% to 50% of breast cancers demonstrate deposits of calcium called microcalcifications. We investigate the potential of using textural features for their correlation with malignancy. A combination of global texture features extracted from the second histogram was combined with local texture features obtained from a wavelet decomposition of the regions containing the calcifications. The performance of the radial-basis-function neural network was compared to the standard multilayered perceptron. The neural networks yielded good results for the classification of hard-to-diagnose cases of mammographic microcalcification into benign and malignant categories using the selected set of features.

ARTIFICIAL NEURAL NETWORK BASED CLASSIFICATION OF MAMMOGRAPHIC MICROCALCIFICATIONS USING IMAGE STRUCTURE FEATURES

Mammography associated with clinical breast examination and breast self-examination is the only effective and viable method for mass breast screening. Most of the minimal breast cancers are detected by the presence of microcalcifications. It is however difficult to distinguish between benign and malignant microcalcifications associated with breast cancer. Most of the techniques used in the computerized analysis of mammographic microcalcifications segment the digitized grey-level image into regions representing microcalcifications. Since mammographic images usually suffer from poorly defined microcalcification features, the extraction of microcalcification features based on segmentation process is not reliable and accurate. We present a second-order grey-level histogram based feature extraction approach which does not require the segmentation of microcalcifications into binary regions to extract features to be used in classification. The image structure features, computed from the second-order grey-level histogram statistics, are used for classification of microcalcifications. Several image structure features were computed for 100 cases of “difficult to diagnose” microcalcification cases with known biopsy results. These features were analyzed in a correlation study which provided a set of five best image structure features. A feedforward backpropagation neural network was used to classify mammographic microcalcifications using the image structure features. Four networks were trained for different combinations of training and test cases, and number of nodes in hidden layers. False Positive (FP) and True Positive (TP) rates for microcalcification classification were computed to compare the performance of the trained networks. The results of the neural network based classification were compared with those obtained using multivariate Baye’s classifiers, and the k-nearest neighbor classifier. The neural network yielded good results for classification of “difficult-to-diagnose” micro-calcifications into benign and malignant categories using the selected image structure features.

Three-dimensional reconstruction of coronary arteries using estimation techniques

Coronary arteriography is a technique used for evaluating the state of the coronary arteries. Matching of coronary arteries from multiple views is necessary for obtaining a 3-D description of the arterial tree. Overlapping vessels and artifacts due to digital subtraction of the angiogram background make the matching process quite difficult. The simplex method applied for linear programming and a relaxation technique for pre-processing the data are applied to skeletons from two views in order to obtain a matching of branches between views. The elements of the centerline along the branch are modeled as a Markov random field and a matching of each element in the two views is obtained by minimizing the energy of the matching contour. The element matching is treated as an estimation problem such that the a- posteriori probability is maximized. Results are provided for the 3-D reconstruction using these algorithms for automatic correspondence, and compared to those obtained by manual correspondence specification. This work was performed using a pig-cast realistic phantom. The results are encouraging.

Segmentation of mammographic microcalcifications

Mammography associated with clinical breast examination and self-breast examination is the only effective and viable method for mass breast screening. Some relevant techFiques to distinguish between benign and malignant microcalcifications are based on the computerized analysis of mammographic microcalcifications. Manual segmentation has been previously used to extract the immediate neighborhood surrounding the microcalcifications from the digitized gray-level image. The method* presented provides an alternative to manual segmentation and shows good robustness to statistical varincions in the background. Algorithms are applied to this segmented region to obtain the microcalcifications. This approach requires selection of a sub-image from the mammogram image. Features can be extracted from the segmented microcalcifications and used for training a neural network for classification of the suspect microcalcifications.

Early detection of postoperative residual tumor using image subtraction

The detection after surgery of residual tumor from magnetic resonance (MR) images is difficult due to the low contrast level of the images. Gadolinium-enhanced MR imaging has been found valuable in detecting residual enhancing tumor when performed within 72 hours after surgery. The patient is scanned by the MR scanner with and without infusion of gadolinium, a contrast agent. Usually, the estimation of post-operative tumor volume is done by visual comparison of the T1 MR images obtained with and without gadolinium infusion. The T1 MR images, in most cases, without contrast demonstrates areas of hyper intensities (high brightness levels), consistent with hemorrhage. These hyper intense areas often make it difficult to detect residual tumor in post contrast images. This is due to the presence of both acute hemorrhage and gadolinium enhancement which have high brightness levels in T1 MR images. Even in MR images taken within 72 hours after surgery, detection of tumor enhancement in areas of increased T1 signal produced by blood products or by postoperative changes can be difficult when performed by the naked eye. Due to these problems, the quantification of residual tumor becomes a subjective issue among neuro-radiologists. Thus to reduce errors produced by the human factor, an automated procedure to detect residual tumor is required. We have developed a technique to differentiate tumor enhancement from postoperative changes and blood products on MR imaging. The technique involves fusion of pre- and post-gadolinium MR images performed in the immediate postoperative period. Computerized slice based substraction is then done on the corresponding fused images of the two sets. The subtraction process results in a composite slice, which is examined for differences between pre- and post-gadolinium studies. The presented technique was tested on 14 cases in which MR images were obtained from brain tumor patients within 72 hours after surgery. The subtraction technique easily distinguished residual enhancing tumor from postoperative surgical changes and was simple to perform. The technique proposed and developed has given good results and will be used in clinical trial and diagnosis. Future potentials of the technique are discussed and illustrative cases presented.