In this paper, we propose an improved fuzzy c-means (FCM) algorithm based on cluster height information to deal with
the sensitivity of unbalanced sized clusters in FCM. As we know, cluster size sensitivity is an major drawback of FCM,
which tends to balance the cluster sizes during iteration, so the center of smaller cluster might be drawn to the adjacent
larger one, which will lead to bad classification. To overcome this problem, the cluster height information is considered
and introduced to the distance function to adjust the conventional Euclidean distance, thus to control the effect on
classification from cluster size difference. Experimental results demonstrate that our algorithm can obtain good
clustering results in spite of great size difference, while traditional FCM cannot work well in such case. The improved
FCM has shown its potential for extracting small clusters, especially in medical image segmentation.
Designed for general purpose with nearly fixed performance, traditional PET systems are constructed with almost
identical and unmovable detectors. In this work, we are developing an application specific PET with detectors with
inhomogeneous performances, which can be adaptively rearranged for different objects and regions of interest (ROIs).
This article reports our initial investigation on a prototype system consisting of inhomogeneous detectors with two levels
of energy resolution. In this system, the high performance detectors and the normal performance detectors are arranged
in one scanner, and the high performance detectors are continuous distributed on the scanner. A liver phantom is
constructed as our object of detection. The coincidence data and image quality are analyzed with different distribution
schemes of the high performance detectors. Preliminary results indicate that the proposed prototype obtains higher true
counts and lower scatter counts than the system with normal performance detectors, resulting in lower scatter fractions
for every region and the whole object. The extent of the reduction of scatter fraction is varied with different distribution
schemes of the high performance detectors, which is related to the distribution of activity. Better signal-to-noise ratio for
every region in the object and better percent contrast are also obtained in some schemes of the high performance
Liver tumor, one of the most wide-spread diseases, has a very high mortality in China. To improve success rates of liver
surgeries and life qualities of such patients, we implement an interactive liver surgery planning system based on contrastenhanced
liver CT images. The system consists of five modules: pre-processing, segmentation, modeling, quantitative
analysis and surgery simulation. The Graph Cuts method is utilized to automatically segment the liver based on an
anatomical prior knowledge that liver is the biggest organ and has almost homogeneous gray value. The system supports
users to build patient-specific liver segment and sub-segment models using interactive portal vein branch labeling, and to
perform anatomical resection simulation. It also provides several tools to simulate atypical resection, including resection
plane, sphere and curved surface. To match actual surgery resections well and simulate the process flexibly, we extend
our work to develop a virtual scalpel model and simulate the scalpel movement in the hepatic tissue using multi-plane
continuous resection. In addition, the quantitative analysis module makes it possible to assess the risk of a liver surgery.
The preliminary results show that the system has the potential to offer an accurate 3D delineation of the liver anatomy, as
well as the tumors' location in relation to vessels, and to facilitate liver resection surgeries. Furthermore, we are testing
the system in a full-scale clinical trial.