Biomedical subjects

A Kandaswamy

Publications and source records attributed to A Kandaswamy.

3 recordsLinked to original sources

PubMed2006

Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms.

An important approach for describing a region is to quantify its structure content. In this paper, the use of functions for computing texture based on statistical measures is described. Six textural features for mammogram images are defined. The segmentation based on these textures would classify the breast tissue under four categories. The algorithm evaluates the region properties of the mammogram image and thereby would classify the image under four important categories based on the intensity level of histograms. Experiments have been conducted on images of mini-MIAS database (Mammogram Image Analysis Society database (UK)). The breast tissue classification thus obtained is comparatively better than the other normal methods. The validation of the work has been done by visual inspection of the segmented image by an expert radiologist. This work is a part of developing a computer aided decision (CAD) system for early detection of breast cancer. The classification results agree with the standard specified by the ACR-BIRADS (American College of Radiology-Breat Imaging And Reporting Data Systems). The accuracy of classification has been found to be 80% as per the visual inspection by an expert radiologist.

Breast↗

PubMed2006

Computer aided decision system for early detection of breast cancer.

BACKGROUND & OBJECTIVES: Mammography is currently the method of choice for early detection of breast cancer in women. However, the interpretation of mammograms is largely based on radiologist's opinion. In this study an attempt is made to develop an image processing algorithm for the detection of microcalcifications and also a computer based decision system for early detection of breast cancer. The proposed method deals with a novel approach for the development of a computer aided decision (CAD) system for early detection of breast cancer by mammogram image analysis. METHODS: The proposed method employs simple thresholding the region of interest and the use of filters for clear identification of microcalcifications. The method suggested for the detection of microcalcifications from a mammogram image segmentation and analysis was tested over several images taken from mini-MIAS (Mammogram Image Analysis Society, UK) database. The algorithm was implemented using Metlab codes programming and hence can work effectively on a simple personal computer with digital mammogram as stored data for analysis. RESULTS: The algorithm works faster so that any radiologist can take a clear decision about the appearance of microcalcifications by visual inspection of digital mammograms. The performance of the algorithm was tested over several images and the validation of results by visual inspection were done by an expert radiologist. Also, the system has given good detection rate as high as 78 percent. The performance analysis of the CAD algorithm was done by receiver operating characteristics (ROC) plot. INTERPRETATION & CONCLUSION: The CAD system suggested here was capable of detecting microcalcifications with a high detection rate, and thus could be used for early detection of breast cancer.

Breast Neoplasms↗

PubMed2004

Neural classification of lung sounds using wavelet coefficients.

Electronic auscultation is an efficient technique to evaluate the condition of respiratory system using lung sounds. As lung sound signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of lung sound signals using wavelet transform, and classification using artificial neural network (ANN). Lung sound signals were decomposed into the frequency subbands using wavelet transform and a set of statistical features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN based system, trained using the resilient back propagation algorithm, was implemented to classify the lung sounds to one of the six categories: normal, wheeze, crackle, squawk, stridor, or rhonchus.

Algorithms↗