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Tessamma Thomas

Publications and source records attributed to Tessamma Thomas.

2 recordsLinked to original sources

Automatic estimation of orientation and position of spine in digitized X-rays using mathematical morphology.

In this paper, we propose a method for automatic determination of position and orientation of spine in digitized spine X-rays using mathematical morphology. As the X-ray images are usually highly smeared, vertebrae segmentation is a complex process. The image is first coarsely segmented to obtain the location and orientation information of the spine. The state-of-the-art technique is based on the deformation model of a template, and as the vertebrae shape usually shows variation from case to case, accurate representation using a template is a difficult process. The proposed method makes use of the vertebrae morphometry and gray-scale profile of the spine. The top-hat transformation-based method is proposed to enhance the ridge points in the posterior boundary of the spine. For cases containing external objects such as ornaments, H-Maxima transform is used for segmentation and removal of these objects. The Radon transform is then used to estimate the location and orientation of the line joining the ridge point clusters appearing on the boundary of the vertebra body. The method was validated for 100 cervical spine X-ray images, and in all cases, the error in orientation was within the accepted tolerable limit of 15 degrees. The average error was found to be 4.6 degrees. A point on the posterior boundary was located with an accuracy of +/-5.2 mm. The accurate information about location and orientation of the spine is necessary for fine-grained segmentation of the vertebrae using techniques such as active shape modeling. Accurate vertebrae segmentation is needed in successful feature extraction for applications such as content-based image retrieval of biomedical images.

Algorithms↗

Multiplexed wavelet transform technique for detection of microcalcification in digitized mammograms.

Wavelet transform (WT) is a potential tool for the detection of microcalcifications, an early sign of breast cancer. This article describes the implementation and evaluates the performance of two novel WT-based schemes for the automatic detection of clustered microcalcifications in digitized mammograms. Employing a one-dimensional WT technique that utilizes the pseudo-periodicity property of image sequences, the proposed algorithms achieve high detection efficiency and low processing memory requirements. The detection is achieved from the parent-child relationship between the zero-crossings [Marr-Hildreth (M-H) detector] /local extrema (Canny detector) of the WT coefficients at different levels of decomposition. The detected pixels are weighted before the inverse transform is computed, and they are segmented by simple global gray level thresholding. Both detectors produce 95% detection sensitivity, even though there are more false positives for the M-H detector. The M-H detector preserves the shape information and provides better detection sensitivity for mammograms containing widely distributed calcifications.

Algorithms↗