Biomedical subjects

Amol S Pednekar

Publications and source records attributed to Amol S Pednekar.

2 recordsLinked to original sources

PubMed2006

Automatic identification of the left ventricle in cardiac cine-MR images: dual-contrast cluster analysis and scout-geometry approaches.

PURPOSE: To evaluate the technical feasibility of two approaches--dual-contrast (DC) cluster analysis, and scout geometry (SG)--for automatic identification of the left ventricular (LV) cavity in short-axis (SA) cine-MR images. MATERIALS AND METHODS: The DC algorithm uses Fuzzy C-Means (FCM) cluster analysis of SA images from a black-blood double-inversion recovery turbo spin-echo (dual IR TSE) sequence, and bright-blood images from a steady-state free precession (SSFP) sequence. The SG algorithm employs geometric information from scout views (i.e., vertical long-axis (VLA) and four-chamber (4CH) views). Both algorithms incorporate additional geometric continuity constraints along with LV region segmentation to identify the LV. The performance of both algorithms was compared on images of eight healthy volunteers, and the SG algorithm was further evaluated on images of 13 clinical patients. RESULTS: The DC algorithm identified the LV in 89% (72/75 at end-diastole (ED) and 47/59 at end-systole (ES)) of the images from healthy volunteers, compared to 98% (74/75 at ED and 57/59 at ES) by the SG algorithm. Both methods are robust against interslice signal variations and misalignment. The DC method suffers from misregistration between the dual IR TSE and SSFP images near the apex at ES. The SG method identified the LV in 91% (112/122 at ED and 91/102 at ES) of the images from clinical patients. CONCLUSION: The SG method requires no additional scan, is robust and accurate, and performs better than the DC method for automatic identification of the LV.

Adult↗

PubMed2006

Image segmentation based on fuzzy connectedness using dynamic weights.

Traditional segmentation techniques do not quite meet the challenges posed by inherently fuzzy medical images. Image segmentation based on fuzzy connectedness addresses this problem by attempting to capture both closeness, based on characteristic intensity, and "hanging togetherness," based on intensity homogeneity, of image elements to the target object. This paper presents a modification and extension of previously published image segmentation algorithms based on fuzzy connectedness, which is computed as a linear combination of an object-feature-based and a homogeneity-based component using fixed weights. We provide a method, called fuzzy connectedness using dynamic weights (DyW), to introduce directional sensitivity to the homogeneity-based component and to dynamically adjust the linear weights in the functional form of fuzzy connectedness. Dynamic computation of the weights relieves the user of the exhaustive search process to find the best combination of weights suited to a particular application. This is critical in applications such as analysis of cardiac cine magnetic resonance (MR) images, where the optimal combination of affinity component weights can vary for each slice, each phase, and each subject, in spite of data being acquired from the same MR scanner with identical protocols. We present selected results of applying DyW to segment phantom images and actual MR, computed tomography, and infrared data. The accuracy of DyW is assessed by comparing it to two different formulations of fuzzy connectedness. Our method consistently achieves accuracy of more than 99.15% for a range of image complexities: contrast 5%-65%, noise-to-contrast ratio of 6%-18%, and bias field of four types with maximum gain factor of up to 10%.

Algorithms↗