An Automated Method for Segmentation of Epithelial Cervical Cells in Images of ThinPrep
Journal of Medical Systems
Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images
Pattern Recognition Letters
Nucleus and cytoplast contour detector from a cervical smear image
Expert Systems with Applications: An International Journal
Leukocyte image segmentation using simulated visual attention
Expert Systems with Applications: An International Journal
A computer assisted method for leukocyte nucleus segmentation and recognition in blood smear images
Journal of Systems and Software
Computer Methods and Programs in Biomedicine
Unsupervised segmentation and classification of cervical cell images
Pattern Recognition
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This paper presents an edge enhancement nucleus and cytoplast contour (EENCC) detector to enable cutting the nucleus and cytoplast from a cervical smear cell image. To clean up noises from an image, this paper proposes a trim-meaning filter that can effectively remove impulse and Gaussian noises but still preserves the sharpness of object boundaries. In addition, a bigroup enhancer is proposed to make a clear-cut separation of the pixels lying in-between two objects. A mean vector difference enhancer is presented to suppress the gradients of noises and also to brighten the gradients of object contours. What is more, a relative-distance-error measure is put forward to evaluate the segmentation error between the extracted and target object contours. The experimental results show that all the aforementioned techniques proposed have performed impressively. Other than for cervical smear images, these proposed techniques can also be utilized in object segmentation of other images.