This paper describes a Computer Aided Diagnosis (CAD) system based on cellphone and distributed cluster. One of
the bottlenecks in building a CAD system for clinical practice is the storage and process of mass pathology samples
freely among different devices, and normal pattern matching algorithm on large scale image set is very time consuming.
Distributed computation on cluster has demonstrated the ability to relieve this bottleneck. We develop a system enabling
the user to compare the mass image to a dataset with feature table by sending datasets to Generic Data Handler Module
in Hadoop, where the pattern recognition is undertaken for the detection of skin diseases. A single and combination
retrieval algorithm to data pipeline base on Map Reduce framework is used in our system in order to make optimal
choice between recognition accuracy and system cost. The profile of lesion area is drawn by doctors manually on the
screen, and then uploads this pattern to the server. In our evaluation experiment, an accuracy of 75% diagnosis hit rate is
obtained by testing 100 patients with skin illness. Our system has the potential help in building a novel medical image
dataset by collecting large amounts of gold standard during medical diagnosis. Once the project is online, the participants
are free to join and eventually an abundant sample dataset will soon be gathered enough for learning. These results
demonstrate our technology is very promising and expected to be used in clinical practice.
The purpose of this study is to investigate the role of shape and texture in the classification of hepatic fibrosis by selecting the optimal parameters for a better Computer-aided diagnosis (CAD) system. 10 surface shape features are
extracted from a standardized profile of liver; while15 texture features calculated from gray level co-occurrence matrix
(GLCM) are extracted within an ROI in liver. Each combination of these input subsets is checked by using support vector machine (SVM) with leave-one-case-out method to differentiate fibrosis into two groups: normal or abnormal.
The accurate rate value of all 10/15 types number of features is 66.83% by texture, while 85.74% by shape features,
respectively. The irregularity of liver shape can demonstrate fibrotic grade efficiently and texture feature of CT image is
not recommended to use with shape feature for interpretation of cirrhosis.