Accurate segmentation of the microscopic cell image is the premise and key of computer-aided diagnosis. However

microscopic images of red blood cells always contain images of strongly adherent

overlapping

and pathological cells

thereby bringing difficulties in accurate segmentation. This study proposes an effective algorithm based on watershed transform to segment these cells in the HSI space. The segmentation accuracy of cell images is improved by adaptively marking the foreground and background areas and reconstructing the gradient maps. The marked watershed transform algorithm is widely used in the segmentations of microscopic cell images because of its simplicity and efficiency. The key to the effectiveness of the algorithm is to accurately mark the foreground cells. However

the presence of strongly adherent

overlapping

and pathological cells seriously affects the accuracy of the markers. To overcome this problem

this study adopts low-pass filtering and proposes an adaptive heuristic algorithm. The proposed algorithm marks the reflective center regions of red blood cells due to non-nuclear characteristics and finds the cytopathic regions from the change in the saturation of pathological cells. Thus

the interference of texture changes in cells caused by uneven illumination and cytopathic regions on segmentation is reduced. Then

in the HSI space

the local extreme points of the low-frequency component are extracted from the gradient maps of the S and I components. The two parts of the reflection regions and the low-frequency extreme points are combined as the initial markers to mark the foreground of image. Thereafter

the pseudo markers are removed in accordance with the different features of these marks to ensure that the overlapped regions of adherent cells are not marked. After obtaining the foreground markers

the background markers are derived from the binary image through morphological operations. Subsequently

a modified gradient map is reconstructed using principal component analysis to extract the information from the S and I components of the gradient map and thus suppress the abnormal gradient value and obtain highly detailed gradients. The reconstructed gradient map retains considerable gradient detail and reduces the noise interference. Finally

the marked watershed transform is applied to the reconstructed gradient map

and image segmentation is realized. An experiment for verifying the effectiveness of the proposed method is conducted. Four types of red blood cell images containing images of strongly adherent

overlapping

and pathological cells from the American Society of Hematology database are utilized for segmentation. Experimental results are quantified through three indexes

namely

average less-segmentation rate

average over-segmentation rate

and average accuracy

in objectively evaluating the performance of different algorithms. The first two indexes of the proposed algorithm are 2.23% and 1.67%

which are significantly lower than the results of the two other watershed algorithms in literature. The segmentation accuracy is as high as 96.10%. The segmentation performance of the proposed algorithm is better than that of the two other algorithms. With regard to timeliness

the average running time of the proposed algorithm is 6.06 s compared with that of the two other algorithms. This study proposes a segmentation algorithm to colored images of red blood cells containing images of strongly adherent

overlapping

and pathological cells. The strongly adherent and overlapping cells can be marked adaptively and accurately by taking advantage of the saturation and brightness information from the S and I components in the HSI space. The principal component analysis can effectively preserve the original boundaries of the overlapping cells. This adaptive marked watershed algorithm not only can separate strongly adherent and overlapping cells but also can effectively suppress the influence of the shape and internal nature of pathological cells on the segmentation. The segmentation results of the proposed algorithm are outstanding in terms of either the accuracy of cell count or the preservation of cell morphology. The performance of the algorithm is insensitive to cytopathic regions and possesses good robustness. This algorithm can be widely used in the segmentation of microscopic staining images with round overlapping and adherent cells as well as blood red cells.