Akihide Hashizume

Segmentation of blood cell image captured by single CCD color TV camera

This paper discusses the classification of blood cell images. The image input method using the single CCD color TV camera and the special color compensation filter, as well as the method of region segmentation for the blood cell images, are described. The following elaborations are made in the image input stage to separate the red blood cell and the white blood cell images in a stable way using optical means. (1) The bluish green light near 450 ∼ 500 nm is cut off from the white light. (2) The accompanying light imbalance between the blue light and the green/red light is compensated by the special color compensation filter. In the (region) segmentation of the blood cell images, a logical operation is developed in which the region of the red blood cell is extracted based on the binary images obtained by the threshold processing of the subtracted image. Using those methods, 59 blood cell images are processed and a satisfactory segmentation result is obtained.

Rotation method for medical three‐dimensional image using object direct operation

This paper proposes a method of rotating a three-dimensional image on a screen by using a two-dimensional pointer (e.g., a mouse cursor) to make medical diagnoses easier. The apparatus rotates the three-dimensional image by directly clicking and dragging areas of interest with a pointer so that a user (e.g., a busy doctor) can examine the image from any number of angles without a break in his/her concentration. Experiments show that the proposed method is faster by 20 percent and simpler and easier to use without special training as compared with a conventional method (in which a mouse cursor points in a numerical viewing angle direction).

Extraction of human stomach using computational geometry

This paper presents a method for extracting the profile of the stomach by computational geometry. The stomach is difficult to recognize from an X-ray because of its elasticity. Global information of the stomach shape is required for recognition. The method has three steps. In the first step, the edge is enhanced, and then edge pieces are found as candidates for the border. Because the resulting border is almost always incomplete, a method for connecting the pieces is required. The second step uses computational geometry to create the global structure from the edge pieces. A Delaunay graph is drawn from the end points of the pieces. This enables us to decide which pieces are most likely to connect. The third step uses the shape of a stomach to find the best sequence of pieces. The knowledge is described in simple LISP functions. Because a Delaunay graph is planar, we can reduce the number of candidate pieces while searching for the most likely sequence. We applied this method to seven stomach pictures taken by the double contrast method and found the greater curvature in six cases. Enhancing the shape knowledge will increase the number of recognizable parts.