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.
We have designed and developed a multiple sclerosis eFolder system for patient data storage, image
viewing, and automatic lesion quantification results stored in DICOM-SR format. The web-based system
aims to be integrated in DICOM-compliant clinical and research environments to aid clinicians in patient
treatments and data analysis. The system needs to quantify lesion volumes, identify and register lesion
locations to track shifts in volume and quantity of lesions in a longitudinal study. In order to perform lesion
registration, we have developed a brain warping and normalizing methodology using Statistical Parametric
Mapping (SPM) MATLAB toolkit for brain MRI. Patients’ brain MR images are processed via SPM’s
normalization processes, and the brain images are analyzed and warped according to the tissue probability
map. Lesion identification and contouring are completed by neuroradiologists, and lesion volume
quantification is completed by the eFolder’s CAD program. Lesion comparison results in longitudinal
studies show key growth and active regions. The results display successful lesion registration and tracking
over a longitudinal study. Lesion change results are graphically represented in the web-based user interface,
and users are able to correlate patient progress and changes in the MRI images. The completed lesion and
disease tracking tool would enable the eFolder to provide complete patient profiles, improve the efficiency
of patient care, and perform comprehensive data analysis through an integrated imaging informatics
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.
To detect the metastatic liver tumor on CT scans, two liver edge maps on unenhanced and portal venous phase images
are firstly extracted and registered using phase-only correlation (POC) method, by which rotation and shift parameters
are detected on two log-polar transformed power spectrum images. Then the liver gray map is obtained on non-contrast
phase images by calculating the gray value within the region of edge map. The initial tumors are derived from the
subtraction of edge and gray maps as well as referring to the score from the spherical gray-level differentiation searching
(SGDS) filter. Finally the FPs are eliminated by shape and texture features. 12 normal cases and 25 cases with 44
metastatic liver tumors are used to test the performance of our algorithm, 86.7% of TPs are successfully extracted by our
CAD system with 2.5 FPs per case. The result demonstrates that the POC is a robust method for the liver registration,
and our proposed SGDS filter is effective to detect spherical shape tumor on CT images. It is expected that our CAD
system could useful for quantitative assessment of metastatic liver tumor in clinical practice.
Primary malignant liver tumor, including hepatocellular carcinoma (HCC), caused 1.25 million deaths per year
worldwide. Multiphase CT images offer clinicians important information about hepatic cancer. The presence of HCC is
indicated by high-intensity regions in arterial phase images and low-intensity regions in equilibrium phase images
following enhancement with contrast material. We propose an automatic method for detecting HCC based on edge
detection and subtraction processing. Within a liver area segmented according to our scheme, black regions are selected
by subtracting the equilibrium phase images to the corresponding registrated arterial phase images. From these black
regions, the HCC candidates are extracted as the areas without edges by using Sobel and LoG edge detection filters. The
false-positive (FP) candidates are eliminated by using six features extracted from the cancer and liver regions. Other FPs
are further eliminated by opening processing. Finally, an expansion process is applied to acquire the 3D shape of the
HCC. The cases used in this experiment were from the CT images of 44 patients, which included 44 HCCs. We extracted
97.7% (43/44) HCCs successfully by our proposed method, with an average number of 2.1 FPs per case. The result
demonstrates that our edge-detection-based method is effective in locating the cancer region by using the information
obtained from different phase images.
Cirrhosis of the liver is characterized by the presence of widespread nodules and fibrosis in the liver. The fibrosis
and nodules formation causes distortion of the normal liver architecture, resulting in characteristic texture patterns.
Texture patterns are commonly analyzed with the use of co-occurrence matrix based features measured on regions-of-interest (ROIs). A classifier is subsequently used for the classification of cirrhotic or non-cirrhotic livers.
Problem arises if the classifier employed falls into the category of supervised classifier which is a popular choice.
This is because the 'true disease states' of the ROIs are required for the training of the classifier but is, generally, not
available. A common approach is to adopt the 'true disease state' of the liver as the 'true disease state' of all ROIs in
that liver. This paper investigates the use of a nonsupervised classifier, the k-means clustering method in classifying
livers as cirrhotic or non-cirrhotic using unlabelled ROI data. A preliminary result with a sensitivity and specificity
of 72% and 60%, respectively, demonstrates the feasibility of using the k-means non-supervised clustering method
in generating a characteristic cluster structure that could facilitate the classification of cirrhotic and non-cirrhotic
Hepatic vessel trees are the key structures in the liver. Knowledge of the hepatic vessel trees is important for liver surgery
planning and hepatic disease diagnosis such as portal hypertension. However, hepatic vessels cannot be easily distinguished
from other liver tissues in non-contrast CT images. Automated segmentation of hepatic vessels in non-contrast CT images
is a challenging issue. In this paper, an approach for automated segmentation of hepatic vessels trees in non-contrast X-ray
CT images is proposed. Enhancement of hepatic vessels is performed using two techniques: (1) histogram transformation
based on a Gaussian window function; (2) multi-scale line filtering based on eigenvalues of Hessian matrix. After the
enhancement of hepatic vessels, candidate of hepatic vessels are extracted by thresholding. Small connected regions of
size less than 100 voxels are considered as false-positives and are removed from the process. This approach is applied to
20 cases of non-contrast CT images. Hepatic vessel trees segmented from the contrast-enhanced CT images of the same
patient are used as the ground truth in evaluating the performance of the proposed segmentation method. Results show that
the proposed method can enhance and segment the hepatic vessel regions in non-contrast CT images correctly.
Segmentation of an abnormal liver region based on CT or MR images is a crucial step in surgical planning. However,
precisely carrying out this step remains a challenge due to either connectivities of the liver to other organs or the shape,
internal texture, and homogeneity of liver that maybe extensively affected in case of liver diseases. Here, we propose a
non-density based method for extracting the liver region containing tumor tissues by edge detection processing. False
extracted regions are eliminated by a shape analysis method and thresholding processing. If the multi-phased images are
available then the overall outcome of segmentation can be improved by subtracting two phase images, and the
connectivities can be further eliminated by referring to the intensity on another phase image. Within an edge liver map,
tumor candidates are identified by their different gray values relative to the liver. After elimination of the small and nonspherical
over-extracted regions, the final liver region integrates the tumor region with the liver tissue. In our experiment,
40 cases of MDCT images were used and the result showed that our fully automatic method for the segmentation of liver
region is effective and robust despite the presence of hepatic tumors within the liver.
The existence of a cluster of microcalcifications in mass area on mammogram is one of important features for distinguishing the breast cancer between benign and malignant. However, missed detections often occur because of its low subject contrast in denser background and small quantity of microcalcifications. To get a higher performance of detecting the cluster in mass area, we combined the shift-invariant artificial neural network (SIANN) with triple-ring filter (TRF) method in our computer-aided diagnosis (CAD) system. 150 region-of- interests around mass containing both of positive and negative microcalcifications were selected for training the network by a modified error-back-propagation algorithm. A variable-ring filter was used for eliminating the false- positive (FP) detections after the outputs of SIANN and TRF. The remained Fps were then reduced by a conventional three layer artificial neural network. Finally, the program identified clustered microcalcifications form individual microcalcifications. In a practical detection of 30 cases with 40 clusters in masses, the sensitivity of detecting clusters was improved form 90% by our previous method to 95% by using both SIANN and TRF, while the number of FP clusters was decreased from 0.85 to 0.40 cluster per image.
We developed a software named LiverANN based on artificial neural network (ANN) technique for distinguishing the pathologies of focal liver lesions in magnetic resonance (MR) imaging, which helps radiologists integrate the imaging findings with different pulse sequences and raise the diagnostic accuracy even with radiologists inexperienced in liver MR imaging. In each patient, regions of focal liver lesion on T1-weighted, T2-weighted, and gadolinium-enhanced dynamic MR images obtained in the hepatic arterial and equilibrium phases were placed by a radiologist (M.K.), then the program automatically calculated the brightness and homogeneity into numerical data within the selected areas as the input signals to the ANN. The outputs from the ANN were the 5 categories of focal hepatic diseases: liver cyst, cavernous hemangioma, dysplasia, hepatocellular carcinoma, and metastasis. Fifty cases were used for training the ANN, while 30 cases for testing the performance. The result showed that the LiverANN classified 5 types of focal liver lesions with sensitivity of 93%, which demonstrated the ability of ANN to fuse the complex relationships among the image findings with different sequences, and the ANN-based software may provide radiologists with referential opinion during the radiologic diagnostic procedure.