Isao Horiba

Linear-time connected-component labeling based on sequential local operations

This paper presents a fast algorithm for labeling connected components in binary images based on sequential local operations. A one-dimensional table, which memorizes label equivalences, is used for uniting equivalent labels successively during the operations in forward and backward raster directions. The proposed algorithm has a desirable characteristic: the execution time is directly proportional to the number of pixels in connected components in an image. By comparative evaluations, it has been shown that the efficiency of the proposed algorithm is superior to those of the conventional algorithms.

Neural edge enhancer for supervised edge enhancement from noisy images

We propose a new edge enhancer based on a modified multilayer neural network, which is called a neural edge enhancer (NEE), for enhancing the desired edges clearly from noisy images. The NEE is a supervised edge enhancer: Through training with a set of input noisy images and teaching edges, the NEE acquires the function of a desired edge enhancer. The input images are synthesized from noiseless images by addition of noise. The teaching edges are made from the noiseless images by performing the desired edge enhancer. To investigate the performance, we carried out experiments to enhance edges from noisy artificial and natural images. By comparison with conventional edge enhancers, the following was demonstrated: The NEE was robust against noise, was able to enhance continuous edges from noisy images, and was superior to the conventional edge enhancers in similarity to the desired edges. To gain insight into the nonlinear kernel of the NEE, we performed analyses on the trained NEE. The results suggested that the trained NEE acquired directional gradient operators with smoothing. Furthermore, we propose a method for edge localization for the NEE. We compared the NEE, together with the proposed edge localization method, with a leading edge detector. The NEE was proven to be useful for enhancing edges from noisy images.

Efficient approximation of neural filters for removing quantum noise from images

In this paper, efficient filters are presented that approximate neural filters (NFs) that are trained to remove quantum noise from images. A novel analysis method is proposed for making clear the characteristics of the trained NF. In the proposed analysis method, an unknown nonlinear deterministic system with plural inputs such as the trained NF can be analyzed by using its outputs when the specific input signals are input to it. The experiments on the NFs trained to remove quantum noise from medical and natural images were performed. The results have demonstrated that the approximate filters, which are realized by using the results of the analysis, are sufficient for approximation of the trained NFs and efficient at computational cost.

A Simple Neural Network Pruning Algorithm with Application to Filter Synthesis

This paper describes an approach to synthesizing desired filters using a multilayer neural network (NN). In order to acquire the right function of the object filter, a simple method for reducing the structures of both the input and the hidden layers of the NN is proposed. In the proposed method, the units are removed from the NN on the basis of the influence of removing each unit on the error, and the NN is retrained to recover the damage of the removal. Each process is performed alternately, and then the structure is reduced. Experiments to synthesize a known filter were performed. By the analysis of the NN obtained by the proposed method, it has been shown that it acquires the right function of the object filter. By the experiment to synthesize the filter for solving real signal processing tasks, it has been shown that the NN obtained by the proposed method is superior to that obtained by the conventional method in terms of the filter performance and the computational cost.

Three-dimensional image reconstruction by digital tomo-synthesis using inverse filtering

Conventional X-ray tomosynthesis with film can provide a sagittal slice image with a single scan. This technique has the advantage of enabling reconstruction of a sagittal slice which is difficult to obtain from the X-ray CT system. However, only an image on the focal plane is obtained by a single scan. Furthermore, the image is degraded by superimpositions of the structures outside of the focal plane. A new three-dimensional image reconstruction method is proposed. This method utilizes a three-dimensional convolution process with an inverse filter function which is derived analytically by the point spread function of the projection and backprojection geometry. A digital tomosynthesis system has also been constructed for the purpose of evaluating the proposed method. This system was used in phantom experiments and clinical evaluations, and it was confirmed that the proposed method was able to reconstruct a better three-dimensional image with less artifacts from outside of the focused slice.

Fast connected-component labeling based on sequential local operations in the course of forward raster scan followed by backward raster scan

Presents a fast algorithm for labeling connected components in binary images based on sequential local operations. A one-dimensional table, which memorizes label equivalences, is used for uniting equivalent labels successively during the operations in forward and backward raster directions. The proposed algorithm has a desirable characteristic: the execution time is directly proportional to the number of pixels in connected components in an image. By comparative evaluations, it has been shown that the efficiency of the proposed algorithm is superior to those of the conventional algorithms.

Efficient approximation of a neural filter for quantum noise removal in X-ray images

An efficient filter approximating the neural filter (NF) trained to remove quantum noise from X-ray images has been realized. A novel analysis method is proposed for making the characteristics of the trained NF clear. It analyses a nonlinear system with plural inputs by using its outputs when the specific input signals are fed to it. The experimental results have demonstrated that the approximate filter, which is realized by using the results of the analysis, is sufficient for approximation of the trained NF, and efficient at computational cost.

Designing the optimal structure of a neural filter

In this paper, we propose a new method for designing the optimal structure of a neural filter (NF), and evaluate its performance. In order to verify the validity of the proposed method, we apply the proposed method to the NF that has been trained to achieve the function of a linear filter with kernel of known shape, and verify that the optimal kernel is achieved. The experimental results demonstrate that the optimized NF by the proposed method achieves the optimal generalization ability, and the performance of the optimized NF by the proposed method is superior to that of the original NF. By the comparative evaluation with the NF that has been trained to reduce noise in medical images, we show that the proposed method is superior to the ability of the conventional method quantitatively in terms of the performance of optimization, the filter performance of the optimized NF, and the generalization ability of the optimized NF.

Contour extraction of left ventricular cavity from digital subtraction angiograms using a neural edge detector

In this paper, a supervised edge detector based on a multilayer neural network, which is called a neural edge detector (NED), is proposed for detecting edges which coincide with edges traced by a human operator (e.g., a medical doctor). The NED is trained by use of the contours traced by a cardiologist. Using the trained NED, the contours coinciding well with the contours traced by a cardiologist are extracted from the left ventricular angiograms even with nonuniform contrast medium. The proposed contour extraction method consists of (1) detection of fine edges by the NED, (2) extraction of rough contours, and (3) contour tracing based on contour candidates synthesized from the rough contours and the edges detected by the NED. The contour of the left ventricle is automatically extracted by inputting two points manually. Experiments with clinical images show that the proposed method can stably extract the contours coinciding well with the contours traced by a cardiologist.

Edge detection from noisy images using a neural edge detector

In this paper, a new edge detector using a multilayer neural network, called a neural edge detector (NED), is proposed for detecting the desired edges clearly from noisy images. The NED is a supervised edge detector: through training the NED with a set of input images and desired edges, it acquires the function of a desired edge detector. The experiments on the NED to detect the edges from noisy test images and noisy natural images were performed. By comparative evaluation with the conventional edge detectors, the following has been demonstrated: the NED is robust against noise; the NED can detect clear continuous edges from the noisy images; and the performance of the NED is the highest in terms of similarity to the desired edges.