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  Indian J Med Microbiol
 

Figure 3: The flow diagram of our automatic stain recognition algorithm. The pixel-wise classification is trained by a foreground stain image and also a background stain image (a). Each pixel of the input image (b) is then classified using nearest neighbor classification to obtain a binary image (c). The small fragments in the binary image are then eliminated by morphological closing and opening, resulting in a smoother output (d). This image is then analyzed by connected component analysis to obtain the center locations of the recognized stains

Figure 3: The flow diagram of our automatic stain recognition algorithm. The pixel-wise classification is trained by a foreground stain image and also a background stain image (a). Each pixel of the input image (b) is then classified using nearest neighbor classification to obtain a binary image (c). The small fragments in the binary image are then eliminated by morphological closing and opening, resulting in a smoother output (d). This image is then analyzed by connected component analysis to obtain the center locations of the recognized stains