Hirokazu Nosato

Anomaly detection for capsule endoscopy images using higher-order Local Auto Correlation features

Capsule endoscopy is a painless way more and more utilized in gastrointestinal examination. Nevertheless, there is an issue comes out that the efficiency and accuracy of capsule endoscopy diagnosis is now restricted by the large quantity of images. In this paper, an anomaly detection method for capsule endoscopy images captured within the range of small intestine is described. Aiming to realize the anomaly detection, this paper takes the advantage of Higher-order Local Auto Correlation features and subspace method using PCA (Principal Component Analysis). The proposed method is validated over capsule endoscopy image sets and its effectiveness is demonstrated by experimental results.

An Extended Method of Higher-order Local Autocorrelation Feature Extraction for Classification of Histopathological Images

In histopathological diagnosis, a clinical pathologist discriminates between normal tissues and cancerous tissues. However, recently, the shortage of clinical pathologists is posing increasing burdens on meeting the demands for such diagnoses, and this is becoming a serious social problem. Currently, it is necessary to develop new medical technologies to help reduce their burdens. Therefore, as a diagnostic support technology, this paper describes an extended method of HLAC feature extraction for classification of histopathological images into normal and anomaly. The proposed method can automatically classify cancerous images as anomaly by using an extended geometric invariant HLAC features with rotation- and reflection-invariant properties from three-level histopathological images, which are segmented into nucleus, cytoplasm and background. In conducted experiments, we demonstrate a reduction in the rate of not only false-negative errors but also of false-positive errors, where a normal image is falsely classified as an image with an anomaly that is suspected as being cancerous.

Histopathological Diagnostic Support Technology Using Higher-Order Local Autocorrelation Features

This paper proposes a technology for histopathological diagnostic support that utilizes the correlation-based features of histopathological tissues. In histopathological diagnosis, a clinical pathologist conducts a diagnosis of normal tissues and cancerous tissues. However, recently, the shortage of clinical pathologists is posing increasing burdens to meet the demands for such diagnoses, and this is causing serious social problems. In order to overcome this problem, we propose a technology of histopathological diagnostic support that uses higher-order local autocorrelation (HLAC) features. The proposed method can automatically screen tissue that is believed to be normal tissue to detect cancerous tissue as well as tissue that is suspected of being cancerous to detect abnormalities. Consequently, we can reduce the burden on clinical pathologists, allowing them to concentrate on diagnosing cancer.

Adaptive Optical Proximity Correction Using an Optimization Method

This paper proposes a new approach to the optical proximity correction (OPC) method which reduces OPC calculation loads by employing an optimization method. OPC is a method of correcting for a mask pattern to improve the fidelity of an image pattern on a silicon wafer. However, conventional OPC calculations have become increasingly complex as the size of semiconductor devices becomes even smaller. In order to overcome this problem, we propose an adaptive OPC technology using an optimization method to realize OPC for the full-chip area at fast operational speeds. The effectiveness of this approach in terms of reduced OPC calculation times and highly-accurate correction is demonstrated through computational experiments.

Automatic Optical Fiber Alignment System Using Genetic Algorithms

We propose and demonstrate an automatic optical fiber alignment system using genetic algorithms. Connecting optical fibers is difficult because the connecting edges should be aligned with sub-micronmeter resolution. It, therefore, takes long time even for a human expert. Although automatic fiber alignment systems are being developed, they cannot be used practically if the degrees of freedom of fiber edges are large. To overcome this difficulty, we have developed an automatic fiber alignment system using genetic algorithms, which incorporate a special local learning method. In experiments, fiber alignment of five degrees of freedom can be completed within a few minutes, whereas it would take a human expert about half an hour.

Hotspot prevention and detection method using an image-recognition technique based on higher-order local autocorrelation

Abstract. Although a number of factors relating to lithography and material stacking have been investigated to realize hotspot-free wafer images, hotspots are often still found on wafers. For the 22-nm technology node and beyond, the detection and repair of hotspots with lithography simulation models is extremely time-consuming. Thus, hotspots represent a critical problem that not only causes delays to process development but also represents lost business opportunities. In order to solve the time-consumption problem of hotspots, this paper proposes a new method of hotspot prevention and detection using an image recognition technique based on higher-order local autocorrelation, which is adopted to extract geometrical features from a layout pattern. To prevent hotspots, our method can generate proper verification patterns to cover the pattern variations within a chip layout to optimize the lithography conditions. Moreover, our method can realize fast hotspot detection without lithography simulation models. Obtained experimental results for hotspot prevention indicated excellent performance in terms of the similarity between generated proposed patterns and the original chip layout patterns, both geometrically and optically. Moreover, the proposed hotspot detection method could achieve turn-around time reductions of >95% for just one CPU, compared to the conventional simulation-based approach, without accuracy losses.

An objective evaluation method of ulcerative colitis with optical colonoscopy images based on higher order local auto-correlation features

This study aims to establish a new method of objective evaluation for optical colonoscopy that can quantify the severity of colonic mucosa for ulcerative colitis (UC). UC is an intractable disease and has been the subject of survey research for long time. However, because there are enormous variations in the patterns of symptoms associated with UC, universal diagnostic standards have yet to be established. Accordingly, diagnostic accuracy is highly dependent on the experience and knowledge of the medical doctor. In order to overcome this problem, this paper describes a method of objective evaluations for UC based on image recognition techniques and multi-discriminant analysis. The proposed method extracts geometrical features using higher order local auto-correlations from the saturation element of the HSV color space for the colonoscopy images, and makes classifications according to the UC severity based on the subspace method. This study provides an index for UC severity to support colonoscopy diagnosis.

Method of retrieving multi-scale objects from optical colonoscopy images based on image-recognition techniques

This paper proposes a method of retrieving multi-scale objects from optical colonoscopy images based on image-recognition techniques. Optical colonoscopy is the most common approach to diagnosing bowel diseases through direct colon and rectum inspections. Periodic optical-colonoscopy examinations are particularly important for detecting cancers at early stages while still treatable. However, diagnostic accuracy is highly dependent on both the experience and knowledge of the medical doctor. Moreover, it is extremely difficult, even for specialist doctors, to detect the early stages of cancer when obscured by inflammations of the colonic mucosa due to intractable inflammatory bowel diseases. In order to assist diagnoses with optical colonoscopy, this paper proposes a retrieval method for colonoscopy images that can cope with multi-scale objects. The proposed method can retrieve similar images despite varying sizes of the target objects. Through experiments conducted with real clinical colonoscopy images, we demonstrate that the method is able to retrieve objects of any size at high levels of accuracy.

A content-based image retrieval method for optical colonoscopy images based on image recognition techniques

This paper proposes a content-based image retrieval method for optical colonoscopy images that can find images similar to ones being diagnosed. Optical colonoscopy is a method of direct observation for colons and rectums to diagnose bowel diseases. It is the most common procedure for screening, surveillance and treatment. However, diagnostic accuracy for intractable inflammatory bowel diseases, such as ulcerative colitis (UC), is highly dependent on the experience and knowledge of the medical doctor, because there is considerable variety in the appearances of colonic mucosa within inflammations with UC. In order to solve this issue, this paper proposes a content-based image retrieval method based on image recognition techniques. The proposed retrieval method can find similar images from a database of images diagnosed as UC, and can potentially furnish the medical records associated with the retrieved images to assist the UC diagnosis. Within the proposed method, color histogram features and higher order local auto-correlation (HLAC) features are adopted to represent the color information and geometrical information of optical colonoscopy images, respectively. Moreover, considering various characteristics of UC colonoscopy images, such as vascular patterns and the roughness of the colonic mucosa, we also propose an image enhancement method to highlight the appearances of colonic mucosa in UC. In an experiment using 161 UC images from 32 patients, we demonstrate that our method improves the accuracy of retrieving similar UC images.

Hotspot detection using image pattern recognition based on higher-order local auto-correlation

Below 40nm design node, systematic variation due to lithography must be taken into consideration during the early stage of design. So far, litho-aware design using lithography simulation models has been widely applied to assure that designs are printed on silicon without any error. However, the lithography simulation approach is very time consuming, and under time-to-market pressure, repetitive redesign by this approach may result in the missing of the market window. This paper proposes a fast hotspot detection support method by flexible and intelligent vision system image pattern recognition based on Higher-Order Local Autocorrelation. Our method learns the geometrical properties of the given design data without any defects as normal patterns, and automatically detects the design patterns with hotspots from the test data as abnormal patterns. The Higher-Order Local Autocorrelation method can extract features from the graphic image of design pattern, and computational cost of the extraction is constant regardless of the number of design pattern polygons. This approach can reduce turnaround time (TAT) dramatically only on 1CPU, compared with the conventional simulation-based approach, and by distributed processing, this has proven to deliver linear scalability with each additional CPU.