Heterotrophic bacteria is detected and counted by National Standard Method of China at present, i.e. flat dish numberation, which has the disadvantages of subjectivity, big error and low efficiency because of relying on manual counting. A method of rapid determination of heterotrophic bacteria in industrial cooling water is developed. After converting the original image into a greyscale one, we removed the edge image of culture dish on the basis of the principle that three unco-linear points can determine a circle. Then binarization with the greyscale-weighted threshold method, distance transform and watershed algorithm to segment overlapping colonies are applied, and finally four-connected searching algorithm was used to mark and count the colony. It has been proved that the method greatly improves the accuracy and efficiency of the heterotrophic bacteria counting and the counting results are not affected by shapes or sizes of the colony. It has been revealed that colony counting based on image processing method can be an effective way.

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Counting method of heterotrophic bacteria based on image processing

A new separating algorithm for quasi-circle overlapping images based on improved Hough transform is proposed in this paper. This method according to a circle’s geometrical property that any point on circumference along the normal direction of the straight line must pass the center of the circle, can extract the central area of the targets, which is used to solve the separating problem of quasi-circle overlapping images. Experiments show that the proposed method can obtain acceptable results.

Separating Algorithm for Quasi-Circle Overlapping Images Based on Improved Hough Transform

The presentation of touching erythrocytes affects the Red Blood Cell counting and diagnosing in automatic blood cell analysis based on digital image processing, thus a robust and efficient recognition algorithm for touching erythrocytes is required. This paper analyses the typical features of touching erythrocytes and presents two new features to characterize them. The algorithm quantifies the radial uniformity of cell outer contours to describe the shape of the erythrocyte, and calculates the contour distribution within sector regions to efficiently approximate the gray scale distribution for texture representation. An ANN classifier was constructed based on the above features, and the discriminating rate for touching erythrocytes achieved 91.82%, which is promising and indicates the efficiency of the proposed features.

Recognition of Touching Erythrocytes via Contour Radial Uniformity and Sector Region Distribution Features

Overlapping appears frequently is all images The algorithm based on the knowledge that red blood cells are disk-shaped Granulometry is employed to estimate cell’s radius R Every contour point corresponds to center point of a circle with radius R estimated with the information of radius and chain code, which will assemble central areas of every single cell Then, morphological dilation with a disk-shaped structuring element of radius R is applied to every central area The intersection of the result of dilation operation and the original binary image is used to estimate the shape of every single cell Experiments show the algorithm can obtain acceptable separating result It can also be used for images of other kind of disk-shaped cells and granules

YU Hui-min

Papers

A Separating Algorithm for Overlapping Red Blood Cell Images