White Matter Lesion Segmentation from Volumetric MR Images

Abstract. White matter lesions are common pathological findings in MR tomograms of elderly subjects. These lesions are typically caused by small vessel diseases (e.g., due to hypertension, diabetes). In this paper, we introduce an automatic algorithm for segmentation of white matter lesions from volumetric MR images. In the literature, there are methods based on multi-channel MR images, which obtain good results. But they assume that the different channel images have same resolution, which is often not available. Although our method is also based on T1 and T2 weighted MR images, we do not assume that they have the same resolution (Generally, the T2 volume has much less slices than the T1 volume). Our method can be summarized as the following three steps: 1) Register the T1 image volume and the T2 image volume to find the T1 slices corresponding to those in the T2 volume; 2) Based on the T1 and T2 image slices, lesions in these slices are segmented; 3) Use deformable models to segment lesion boundaries in those T1 slices, which do not have corresponding T2 slices. Experimental results demonstrate that our algorithm performs well.

Morphological Operations on 3D and 4D images: from shape primitive detection to skeletonization

. This paper describes a practical approach to mathematical morpholog

Level Set Contouring for Breast Tumor in Sonography

The echogenicity, echotexture, shape, and contour of a lesion are revealed to be effective sonographic features for physicians to identify a tumor as either benign or malignant. Automatic contouring for breast tumors in sonography may assist physicians without relevant experience, in making correct diagnoses. This study develops an efficient method for automatically detecting contours of breast tumors in sonography. First, a sophisticated preprocessing filter reduces the noise, but preserves the shape and contrast of the breast tumor. An adaptive initial contouring method is then performed to obtain an approximate circular contour of the tumor. Finally, the deformation-based level set segmentation automatically extracts the precise contours of breast tumors from ultrasound (US) images. The proposed contouring method evaluates US images from 118 patients with breast tumors. The contouring results, obtained with computer simulation, reveal that the proposed method always identifies similar contours to those obtained with manual sketching. The proposed method provides robust and fast automatic contouring for breast US images. The potential role of this approach might save much of the time required to sketch a precise contour with very high stability