Takanori Koga

Automated boundary extraction and visualization system for coronary plaque in IVUS image by using fuzzy inference-based method

We propose a fully automatic plaque boundary extraction system for an intravascular ultrasound (IVUS) image aiming at practical use in clinic. The IVUS image, which is commonly used for a diagnosis of acute coronary syndromes (ACS) in the field of cardiology, has coarse-grained texture due to heavy speckle noise. A medical doctors interpretation of the IVUS image is disturbed frequently by the heavy speckle noise. In the proposed system, the heavy speckle noise is reduced firstly by using an anisotropic diffusion filter. Secondarily, the plaque boundary is extracted by using the Takagi-Sugeno (T-S) type fuzzy inference with a weighted separability measure and some heuristic rules. Extraction of plaque boundary is achieved fully automatically. The proposed system substantially reduces the workload of medical doctors. The effectiveness of the proposed system has been verified by the experiments using the real IVUS images.

Tissue characterization of coronary plaque by kNN classifier with fractal-based features of IVUS RF-signal

We propose a tissue characterization method for coronary plaques by using fractal analysis-based features. Those features are obtained from radiofrequency (RF) signals measured by the intravascular ultrasound (IVUS) method. The IVUS method is used for the diagnosis of the acute coronary syndrome. In the proposed method, the fact that the complexity of the tissue structures is reflected in the RF signals is used. The effectiveness of the proposed method is verified through some experiments by using IVUS RF signals obtained from rabbits and human patients.

Fully Automatic Boundary Extraction of Coronary Plaque in IVUS Image by Anisotropic Diffusion and T-S Type Fuzzy Inference

This paper describes a fully automatic plaque boundary extraction method for the intravascular ultrasound image by using anisotropic-diffusion-based preprocessing and Takagi-Sugeno (T-S) type fuzzy inference. In the pre-processing, areas for plaque boundary extraction are automatically searched and found by image processing and some heuristic rules. In the found areas, the objective boundaries are then extracted by T-S type fuzzy inference. The present method has reduced substantially the workload of medical doctors.

Image coarsening by using space-filling curve for decomposition-based image enhancement

We propose a novel space-filling curve based image coarsening method, which automatically extracts a base-layer from an input image while still preserving its structural context, meaningful details, et cetera. In the proposed method, specifically, a one-dimensional edge-preserving smoothing filter, which is called a vector @e-filter, is applied to an input image along a space-filling curve. In this regard, the space-filling curve is constructed by using a minimum spanning tree which extracts the structural context of the input image. This novel image coarsening approach is completely different from all conventional approaches employing any kind of two-dimensional filter window. Furthermore, this coarsening method can effectively produce an aggregation of texture details as well as enhance sharp edges, while preserving structural contexts such as thin lines and sharp corners. The main benefit of the coarsened image by the proposed method is its suitability for extracting fine features of an input image for decomposition-based image enhancement. In this paper, the structural-context-preserving image coarsening capability of the proposed method is verified by some results from experiments and examples. Then we show our new methods characteristics in practical application to decomposition-based image enhancement by using some other examples.

Automatic Plaque Boundary Extraction in Intravascular Ultrasound Image by Fuzzy Inference with Adaptively Allocated Membership Functions

This paper describes an automatic plaque boundary extraction in the intravascular ultrasound image by a fuzzy inference. In the proposed method, the membership functions in the antecedent parts of the fuzzy rules are adaptively allocated by using the information of the seed points given by a medical doctor. The present method not only improved the accuracy of plaque boundary extraction but also reduced the workload of medical doctors.

Structural-context-preserving image abstraction by using space-filling curve based on minimum spanning tree

We propose a novel image abstraction method which generates a non-photographic painting-like image automatically from a photographic natural image while preserving its structural context, detailed structure, and so on. In the proposed method, concretely, one-dimensional vector є-filter is applied to an input image along a space-filling curve. This spacefilling curve is constructed by using a minimum spanning tree reflecting structural context of the input image. This novel image abstraction approach is completely different from conventional approaches. By using the proposed method, aggregation of texture details and enhancement of sharp edges are effectively realized while preserving structural contexts such as thin lines, sharp corners, and so on. The structural-context-preserving abstraction capability of the proposed method is verified by some experimental results and examples.

Particle Swarm Optimization with SIMD-Oriented Fast Mersenne Twister on the Cell Broadband Engine

We introduce a processing performance of Particle Swarm Optimization with SIDM-oriented Fast Mersenne Twister on the Cell Broadband Engine. Extreme-high processing performance is demanded for solving very complex optimization problem in a small amount of time. In this research, we verified the effectiveness of employing SIMD-oriented Fast Mersenne Twister on the Cell Broadband Engine for the processing of Particle Swarm Optimization by numerical simulations.

Weighted median filter with minimum spanning tree-based adaptive window

We propose an extended weighted median filter which removes random-valued impulse noise with preserving detailed structures in a grayscale image. This filter combines the median filter with a square window and that with adaptive ones constructed by using 8-neighborhood minimum spanning trees (MSTs). The former one has superior capability in the impulse noise removal. However, it tends to destroy detailed structures in an image. To cope with this problem, the proposed method calculates a weighted median by using the pixels both in the square window and the adaptive one which fits to a detailed structure in the image. In this paper, the superior performance of the proposed method is verified through the experiments with grayscale natural and artificial images by comparing with some MST-based noise removal methods.

Random-valued impulse noise removal in color images by using switching Non-local vector median filter

This paper describes a novel image filtering method for removal of random-valued impulse noise superimposed on color images. In color image filtering, generally, it is preferable to deal with the R, G, and B components of each pixel of a color image as elements of a vectorized signal as in the vector median filter rather than component-wise signals in order to prevent color shift after filtering. Furthermore, in impulse noise removal, it is essential to use switching type filtering manner as in the switching median filter to preserve the detailed part of an original image with good quality. By taking those fundamentals in consideration, in this study, we propose a switching-type vector median filter with non-local processing mainly consists of a noise detection part and a noise removal one. Concretely, we propose a noise detector which proactively finds out noise-corrupted pixels by focusing attention on isolation tendencies of pixels of interest in difference images between RGB components. Furthermore, as a noise removal filter, we propose an extended version of Non-local median filter (NL-MF), which we proposed previously for grayscale images, named Non-local vector median filter (NL-VMF) which deals with color images. The proposed method realizes superior balance between the detail-preservation based on the proactive noise detection and the noise removal by the non-local switching vector median filtering. The effectiveness and the validity of the proposed method are verified by some experiments using natural color images.

Impulse noise removal by using one-dimensional switching median filter applied along space-filling curve reflecting structural context of image

A switching median filter (SMF) is effective for impulse noise removal while still preserving edges in an input image. This filter firstly detects pixels corrupted by impulse noise and then filters only the noise-corrupted pixels. However the noise detection process does not always work perfectly. Particularly, pixels constituting thin lines in an input image tend to be incorrectly detected as noise-corrupted pixels, and such pixels are filtered despite the needlessness of the filtering. As the result of the filtering, the image might be over-smoothed and be deteriorated throughout the entire image. To cope with this problem, we propose a new impulse noise removal method based on a one-dimensional SMF and a space-filling curve which reflects structural contexts of an input image. The effectiveness of the proposed method is verified by some experiments.