Xian-Hua Han

Pilot study of applying shape analysis to liver cirrhosis diagnosis

This paper explores the potential of applying shape analysis to classify normal/cirrhotic liver and in addition estimate the severity of abnormal cases. Conventional Computer-Aided Diagnosis (CAD) systems are developed for automatically providing a binary output as a second opinion to assist radiologists to draw conclusions about the condition of the pathology (normal or abnormal). After the disease is diagnosed, grasping the proceeding stage of the abnormal degree is essential for adopting the appropriate strength of treatment. However, none of existing CAD system is well established for such a challenging task. Liver cirrhosis has an important feature: morphological changes of the liver and the spleen occur during the clinical course of liver cirrhosis. In this study we constructed liver, spleen and their joint Statistical Shape Models (SSMs) to quantitatively assess the global shape variation and selected several modes from the SSMs. Then we learnt a mapping function between coefficients of selected modes and the ground truth staging label by Support Vector Regression (SVR). Using this mapping function, the proceeding stage of new input data can be estimated. Experimental results have validated the potential of our method on assisting the cirrhosis diagnosis.

A novel and fast connected component count algorithm based on graph theory

A fast component-counting algorithm is proposed based on graph theory in this paper. We derived a formulation to count faces in a plane given only the vertices based on Euler polyhedron formula. Vertices with degree no more than two are ineffective in counting components. With the derived formula, a graph component counting algorithm is constructed based only on searching cross points whose degree are no less than three. When applied to a two-dimensional binary image, the proposed method divides an image into patches of same size and decides which of them will be used in counting by searching the circumferential pixels of each patch. If the number of component edges within the circumferential pixels of a patch is no less than three, then the patch will be used in counting. After determining all the vertices with degree no less than three, the number of components can be calculated by the formula. Because only a small number of pixels are investigated in the process, the computational time is very fast. The disconnection of edges in an image is one of the main reasons which causes miscounts for scanning-based algorithms. The difficulty, however, can be naturally overcome by the proposed algorithm because disconnected points will be identified as futile pixels in the algorithm. Experimental results show the algorithm is more efficient than existing methods. When applied to images with disconnected edges, the counted number given by scanning-based algorithms is much smaller than the correct number whereas the proposed algorithm obtains satisfactory results.

Enhancement of IVR images by combining an ICA shrinkage filter with a multi-scale filter

Interventional Radiology (IVR) is an important technique to visualize and diagnosis the vascular disease. In real medical application, a weak x-ray radiation source is used for imaging in order to reduce the radiation dose, resulting in a low contrast noisy image. It is important to develop a method to smooth out the noise while enhance the vascular structure. In this paper, we propose to combine an ICA Shrinkage filter with a multiscale filter for enhancement of IVR images. The ICA shrinkage filter is used for noise reduction and the multiscale filter is used for enhancement of vascular structure. Experimental results show that the quality of the image can be dramatically improved without any blurring in edge by the proposed method. Simultaneous noise reduction and vessel enhancement have been achieved.