We propose new lossless medical image compression method based on hierarchical sorting technique. Hierarchical sorting is a technique to achieve high compression ratio by detecting the regions where image pattern varies abruptly and sorting pixel order by its value to increase predictability. In this method, we can control sorting accuracy along with size and complexity. As the result, we can reduce the sizes of the permutation-tables and reuse the tables to other image regions. Comparison using experimental implementation of this method shows better performance for medical image set measured by X-ray CT and MRI instruments where similar sub-block patterns appear frequently. This technique applies quad-tree division method to divide an image to blocks in order to support progressive decoding and fast preview of large images.

Functional magnetic resonance imaging (fMRI) allows to display functional activities of certain brain areas. In combination with a three dimensional anatomical dataset, acquired with a standard magnetic resonance (MR) scanner, it can be used to identify eloquent brain areas, resulting in so-called functional neuronavigation, supporting the neurosurgeon while planning and performing the operation. But during the operation brain shift leads to an increasing inaccuracy of the navigation system. Intraoperative MR imaging is used to update the neuronavigation system with a new anatomical dataset. To preserve the advantages of functional neuronavigation, it is necessary to save the functional information. Since fMRI cannot be repeated intraoperatively with the unconscious patient easily we tried to solve this problem by means of image processing and pattern recognition algorithms. In this paper we present an automatic approach for transfering preoperative markers into an intraoperative 3-D dataset. In the first step the brains are segmented in both image sets which are then registered and aligned. Next, corresponding points are determined. These points are then used to determine the position of the markers by estimating the local influence of brain shift.

This paper describes a method for classification of hematoxylin and eosin (HE)-stained breast tumor images into benign or malignant using the adaptive searching ability of artificial organisms. Each artificial organism has some attributes, such as, age, internal energy and coordinates. In addition, the artificial organism has a differentiation function for evaluating "malignant" or "benign" tumors and the adaptive behaviors of each artificial organism are evaluated using five kinds of texture features. The texture feature of nuclei regions in normal mammary glands and that of carcinoma regions in malignant tumors are treated as "self" and "non-self," respectively. This model consists of two stages of operations for detecting tumor regions, the learning and searching stages. At the learning stage, the nuclei regions are roughly detected and classified into benign or malignant tumors. At the searching stage, the similarity of each organism's environment is investigated before and after the movement for detecting breast tumor regions precisely. The method developed was applied to 21 cases of test images and the distinction between malignant and benign tumors by the artificial organisms was successful in all cases. The proposed method has the following advantages: the texture feature values for the evaluation of tumor regions at the searching stage are decided automatically during the learning stage in every input image. Evaluation of the environment, whether the target pixel is a malignant tumor or not, is performed based on the angular difference in each texture feature. Therefore, this model can successfully detect tumor regions and classify the type of tumors correctly without affecting a wide variety of breast tumor images, which depends on the tissue condition and the degree of malignancy in each breast tumor case.

This paper describes an automatic region segmentation method which is detectable nuclei regions from hematoxylin and eosin (HE)-stained breast tumor images using artificial organisms. In this model, the stained images are treated as virtual environments which consist of nuclei, interstitial tissue and background regions. The movement characteristics of each organism are controlled by the gene and the adaptive behavior of each organism is evaluated by calculating the similarities of the texture features before and after the movement. In the nuclei regions, the artificial organisms can survive, obtain energy and produce offspring. Organisms in other regions lose energy by the movement and die during searching. As a result, nuclei regions are detected by the collective behavior of artificial organisms. The method developed was applied to 9 cases of breast tumor images and detection of nuclei regions by the artificial organisms was successful in all cases. The proposed method has the following advantages: (1) the criteria of each organism's texture feature values (supervised values) for the evaluation of nuclei regions are decided automatically at the learning stage in every input image; (2) the proposed algorithm requires only the similarity ratio as the threshold value when each organism evaluates the environment; (3) this model can successfully detect the nuclei regions without affecting the variance of color tones in stained images which depends on the tissue condition and the degree of malignancy in each breast tumor case.

This paper describes a new region segmentation method which is detectable carcinoma regions from hematoxylin and eosin (HE)-stained breast tumor images using collective behaviors of artificial organisms. In this model, the movement characteristics of artificial organisms are controlled by the gene, and the adaptive behavior of artificial organisms in the environment, carcinoma regions or not, is evaluated by the texture features.

In this paper we propose a new medical image processing system called Virtualized Endoscope System (VES)", which can examine the inside of a virtualized human body. The virtualized human body is a 3-D digital image which is taken by such as X-ray CT scanner or MRI scanner. VES consists of three modules; (1) imaging, (2) segmentation and reconstruction and (3) interactive operation. The interactive operation module has following thee major functions; (a) display of, (b) measurement from, and (c) manipulation to the virtualized human body. The user of the system can observe freely both the inside and the outside of a target organ from any point and any direction freely, and can perform necessary measurement interactively concerning angle and length at any time during observation. VES enables to observe repeatedly an area where the real endoscope can not enter without pain from any direction which the real endoscope can not. We applied this system to real 3-D X-ray CT images and obtained good result.

In this paper, the discrimination of ultrasonic heart (echocardiographic) images is studied by making use of some texture features, including the angular second moment, contrast, correlation and entropy which are obtained from a gray-level cooccurrence matrix. Features of these types are used as inputs to the input layer of a neural network (NN) to classify two sets of echocardiographic images-normal heart and dilated cardiomyopathy (DCM) (18 and 13 samples, respectively). The performance of the NN classifier is also compared to that of a minimum distance (MD) classifier. Implementation of our algorithm is performed on a PC-486 personal computer. Our results show that the NN produces about 94% (the confidence level setting is 0.9) and the MD produces about 84% correct classification. We notice that the NN correctly classifies all the DCM cases, namely, all the misclassified cases are of false positive. These results indicate that the method of feature-based image analysis using the NN has potential utility for computer-aided diagnosis of the DCM and other heart diseases.

In this letter the classification of echocardiographic images is studied by making use of some texture features, including the angular second moment, the contrast, the correlation, and the entropy which are obtained from a gray-level cooccurrence matrix. Features of these types are used to classify two sets of echocardiographic images-normal and abnormal (cardiomyopathy) hearts. A minimum distance classifier and evaluation indexes are employed to evaluate the performance of these features. Implementation of our algorithm is performed on a PC-386 personal computer and produces about 87% correct classification for the two sets of echocardiographic images. Our preliminary results suggest that this method of feature-based image analysis has potential use for computer-aided diagnosis of heart diseases.

This paper describes a segmentation method of liver structure from abdominal CT images using a three–layered neural network (NN). Before the NN segmentation, preprocessing is employed to locally enhance the contrast of the region of interest. Postprocessing is also automatically applied after the NN segmentation in order to remove the unwanted spots and smooth the detected boundary. To evaluate the performance of the proposed method, the NN–determined boundaries are compared with those traced by two highly trained surgeons. Our preliminary results show that the proposed method has potential utility in automatic segmentation of liver structure and other organs in the human body.

This paper describes a color segmentation method which is essential for automatic diagnosis of stained images. This method is applicable to the variance of input images using a three-layered neural network model. In this network, a back-propagation algorithm was used for learning, and the training data sets of RGB values were selected between the dark and bright images of normal mammary glands. Features of both normal mammary glands and breast cancer tissues stained with hematoxylin-eosin (HE) staining were segmented into three colors. Segmented results indicate that this network model can successfully extract features at various brightness levels and magnifications as long as HE staining is used. Thus, this color segmentation method can accommodate change in brightness levels as well as hue values of input images. Moreover, this method is effective to the variance of scaling and rotation of extracting targets.