Cirrhosis of the liver is a chronic disease. It is characterized by the presence of widespread nodules and fibrosis in
the liver which results in characteristic texture patterns. Computerized analysis of hepatic texture patterns is usually
based on regions-of-interest (ROIs). However, not all ROIs are typical representatives of the disease stage of the
liver from which the ROIs originated. This leads to uncertainties in the ROI labels (diseased or non-diseased). On
the other hand, supervised classifiers are commonly used in determining the assignment rule. This presents a
problem as the training of a supervised classifier requires the correct labels of the ROIs. The main purpose of this
paper is to investigate the use of an unsupervised classifier, the k-means clustering, in classifying ROI based data.
In addition, a procedure for generating a receiver operating characteristic (ROC) curve depicting the classification
performance of k-means clustering is also reported. Hepatic MRI images of 44 patients (16 cirrhotic; 28 non-cirrhotic)
are used in this study. The MRI data are derived from gadolinium-enhanced equilibrium phase images.
For each patient, 10 ROIs selected by an experienced radiologist and 7 texture features measured on each ROI are
included in the MRI data. Results of the k-means classifier are depicted using an ROC curve. The area under the
curve (AUC) has a value of 0.704. This is slightly lower than but comparable to that of LDA and ANN classifiers
which have values 0.781 and 0.801, respectively. Methods in constructing ROC curve in relation to k-means
clustering have not been previously reported in the literature.
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
The identification of mammary gland regions is a necessary processing step during the anatomical structure
recognition of human body and can be expected to provide the useful information for breast tumor diagnosis. This paper
proposes a fully-automated scheme for segmenting the mammary gland regions in non-contrast torso CT images. This
scheme calculates the probability for each voxel belonging to the mammary gland or other regions (for example
pectoralis major muscles) in CT images and decides the mammary gland regions automatically. The probability is
estimated from the location of the mammary gland and pectoralis major muscles in CT images. The location (named as a
probabilistic atlas) is investigated from the pre-segmentation results in a number of different CT scans and the CT
number distribution is approximated using a Gaussian function. We applied this scheme to 66 patient cases (female, age:
40-80) and evaluated the accuracy by using the coincidence rate between the segmented result and gold standard that is
generated manually by a radiologist for each CT case. The mean value of the coincidence rate was 0.82 with the standard
deviation of 0.09 for 66 CT cases.
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.
A co-occurrence matrix is a joint probability distribution of the pixel values of two pixels in an image separated by a distance d in the direction θ. It is one of the texture analysis tools favored by the medical image processing community. The size of a co-occurrence matrix depends on gray levels re-quantization Q. Hence, when dealing with high depth
resolution images, gray levels re-quantization is routinely performed to reduce the size of the co-occurrence matrix. The gray levels re-quantization may play a role in the display of spatial relationships in co-occurrence matrix but is usually dealt with lightly. In this paper, we use an example to study the effect of gray-level re-quantization in high depth resolution medical images. Digitized film-screen mammograms have a typical depth resolution of 4096 gray levels. In a study classifying masses on mammograms as benign or malignant, 260 texture features are measured on 43 regions-of-interest (ROIs) containing malignant masses and 28 ROIs containing benign masses. Of the 260 texture features,
240 are texture features measured on co-occurrence matrices with parameters θ = 0, π/2; d = 11, 15, 21, 25, 31; and Q = 50, 100, 400. A genetic algorithm is used to select a subset of features (out of 260) that has discriminative power. Results show that top performing feature combinations selected by the genetic algorithm are not restricted to a single value of Q. This indicates that instead of searching for a correct Q, it may be more appropriate to explore a range of