Abdulfattah A. Aboaba

Evolution of brain tumor growth model: A back-in-time approach

One of the crucial stages in Image Guided Surgery (IGS) protocol is the IGS planning stage, the major requirements at that stage are to determine tumor extent and size by reconstructing tumor slices, and to determine the best way to approach affected site. The surgeon is clinically responsible to do these with the aid of IGS software that is fashioned in line with the surgeon training. It has been observed that lots of time used at that stage could be saved if a good model that will accurately estimate tumor growth from few slices could be developed to assist at that stage. This paper presents a tumor growth modeling approach based on step response of electrical energy devices, and its application to tumor estimation using non-IGS protocol image slices to produce tumor area estimation that is comparable for image reconstruction purposes with tumor area found on the thin slice IGS protocol images, thereby eliminating the need for time consuming IGS protocol imaging.

Hybrid and Multilevel Segmentation Technique for Medical Images

In this paper, we present a novel, fast, hybrid and bi-level segmentation technique uniquely developed for segmentation of medical images. Medical images are generally characterized by multiple regions, and weak edges. When regions in medical images are viewed as made up of homogeneous group of intensities, it becomes more difficult to analyze because quite often different organs or anatomical structures may have similar gray level or intensity representation. The complexity of medical imagery is well catered for in this technique by starting-out with multiple thresholding, applying similarity segmentation method, and resolving boundary problem with template matching technique, and then a region of interest (ROI) segmentation that involves finding the edges of the object of interest (OOI) at final stage. This technique can also be adapted to segmentation of non-medical images. A job is run using MATLAB and simple Grid computing as suitable environment.