Qolamreza 'Ray' Razlighi, Ph.D.
Instructor in Neurology
The Taub Institute
630 West 168th Street
New York, NY 10032
- Causal Markov random field. Conventional Markov random field is hampered by noncausality and its causal definitions are also not free of difficulties. For instance, the Markov mesh random field has strong diagonal dependency in its local neighboring system. I have introduced multilateral Markov random field (MMRF) to overcome this issue. MMRF is a MRF whereas the reverse may not hold. The joint distribution of a causal MRF is readily given by the product of the low-dimensional local distribution whereas in conventional MRF it is only given through Gibbs distribution. Low-dimensional joint pdf's are often estimated using a joint histogram for homogeneous field or by a few sample of the field for inhomogeneous fields. This makes the model closely tied to the image in use. So far MMRF is used for the computation of image entropy and mutual information.
- Feature Extraction/Matching for brain images. Neuroanatomical features of the human brain shown to be useful in understanding, diagnosis, or prediction of the treatment outcome. Quantification of the human brain morphology is deeply depend on the robustness of the extracted features. It is useful for neuro- scientists and clinical researchers to identify and/or quantify cortical folding patterns across individuals. The top (gyri) and bottom (sulci) of these folds resemble the "blob-like" features used in computer vision. We introduce the "brain blob detector and descriptor (BBDD)." For the first time blob detectors are considered as spatial filters under the scale-space framework and their impulse responses are manipulated for detecting the structures in our interest. The BBDD detector is tailored to the scale and structure of blob-like features that coincide with cortical folds, and its descriptors performed well at discriminating these features in our evaluation.
1. Q. R. Razlighi and N. Kehtarnavaz, “A comparison study of image spatial entropy”, Proceedings of the SPIE
Conference on Visual Communications and Image Processing, San Jose, Jan. 2009.
2. Q. R. Razlighi, N. Kehtarnavaz, and A. Nosratinia, “Computation of image spatial entropy using quadrilateral Markov random field”, IEEE Transaction on Image Processing, vol. 18, no. 12, Dec. 2009.
3. Q. R. Razlighi, M. T. Rahman, and N. Kehtarnavaz, “Fast Computation Methods for Estimation of Image Spatial Entropy”, Journal of Real-Time Image Processing, vol. 6, no. 2, pp. 137-142, 2011.
4. M. Rahman, N. Kehtarnavaz, Q. R. Razlighi, “Using image entropy maximum for auto exposure”, SPIE Journal of Electronic Imaging, Appeared Online, Feb. 8, 2011.
5. Q. R. Razlighi and Y. Stern, “Blob-like feature extraction and matching for brain MR images,” Proceedings of IEEE Engineering in Medicine and Biology Society, Boston, Aug. 2011.