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.

This paper presents a design of low-power CMOS OTA-C filters suitable for on-chip integration of advanced monolithic system LSIs that have analog I/O and digital signal processing capability. First, we discuss the distortion of MOS cross-coupled circuits which have a quite low distortion when the MOS FETs have the square law characteristics. Considering the nonidealities of MOS FET, however, we find that the third harmonic component of signal dominates the total harmonic distortion (THD) of the cross-coupled pair circuit. We propose a new architecture to reduce the 3rd harmonic component. Low distortion operational transconductance amplifiers (OTA) which consist of the proposed low distortion cross-coupled pair are applied to the realization of OTA-Capacitor filters. The SPICE simulation shows that the THD of the filter is 0.0047% and the power dissipation is 22.6 mW.

A 7 bit 1.0 Gsps Cascaded Folding ADC is presented. This ADC employs cascaded folding architecture with 3-degree folders. A new reset technique and layout shuffling enable the ADC to operate at high-speed with low power consumption. Implemented in a 90 nm CMOS process technology the ADC consumes 230 mW with 1.2 V and 2.5 V supplies and has a SNR of 38 dB.

In this paper, we propose a new computer-aided diagnosis system which can extract specific features from hematoxylin and eosin (HE)-stained breast tumor images and evaluate the type of tumor using artificial organisms. The gene of the artificial organisms is defined by three kinds of texture features, which can evaluate the specific features of the tumor region in the image. The artificial organisms move around in the image and investigate their environmental conditions during the searching process. When the target pixel is regarded as a tumor region, the organism obtains energy and produces offspring; organisms in other regions lose energy and die. The searching process is iterated until the 30th generation; as a result, tumor regions are filled with artificial organisms. Whether the detected tumor is benign or malignant is evaluated based on the combination of selected genes. The method developed was applied to 27 test cases and the distinction between benign and malignant tumors by the artificial organisms was successful in about 90% of tumor images. In this diagnosis support system, the combination of genes, which represents specific features of detected tumor region, is selected automatically for each tumor image during the searching process.

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.

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 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.