Automatic segmentation of cell nuclei is an essential task in a variety of biomedical applications. However, performance is variable, being typically around 95% correct detection of nuclei depending on the degree of clustering. Furthermore, work to assess and optimize the segmentation accuracy of each nucleus is in its infancy. In this study we developed an algorithm intended for segmentation o...

Automatic image segmentation of immunohistologically stained breast tissue sections helps pathologists to discover the cancer disease earlier. The detection of the real number of cancer nuclei in the image is a very tedious and time consuming task. Segmentation of cancer nuclei, especially touching nuclei, presents many difficulties to separate them by traditional segmentation algorithms. This ...

OBJECTIVE To develop a computer-aided robust nuclei segmentation technique for quantitative histopathological image analysis. STUDY DESIGN A robust nuclei segmentation technique for histopathological image analysis is proposed. The proposed technique uses a hybrid morphological reconstruction module to reduce the intensity variation within the nuclei regions and suppress the noise in the imag...

acute lymphoblastic leukemia is the most common form of pediatric cancer which is categorized into three l1, l2, and l3 and could bedetected through screening of blood and bone marrow smears by pathologists. due to being time‑consuming and tediousness of theprocedure, a computer‑based system is acquired for convenient detection of acute lymphoblastic leukemia. microscopic images areacquired fro...

Several recent studies explored the use of unsupervised segmentation methods for segmenting thalamic nuclei from diffusion tensor images. These methods provide a plausible segmentation on individual subjects; however, they do not address the problem of consistently identifying the same functional areas in a population. The lack of correspondence between the segmented nuclei make it more difficu...

Abstract Accurate nuclei segmentation, as an indispensable basis and core link for multi-cell cervical image analysis, plays important role in automatic pre-cancer detection. However, poor quality due to the uneven staining, complex backgrounds overlapped cell clusters poses a great challenge segmentation. In this paper, we propose new Selective-Edge-Enhancement-based Nuclei Segmentation method...

This paper proposes an unsupervised segmentation scheme for cell nuclei. This method computes the cell nuclei by using adaptive active contour modelling which is driven by the morphology method. Firstly, morphology is used to enhance the gray level values of cell nuclei. Then binary cell nuclei is acquired by using an image subtraction technique. Secondly, the masks of cell nuclei are utilized ...

This paper addresses the task of nuclei segmentation in high-resolution histopathological images. We propose an automatic end-to-end deep neural network algorithm for segmentation of individual nuclei. A nucleus-boundary model is introduced to predict nuclei and their boundaries simultaneously using a fully convolutional neural network. Given a color normalized image, the model directly outputs...

Introduction: Fast and accurate segmentation of thalamic nuclei is important for clinical applications such as treatment of Parkinson's disease or chronic pain syndrome for example. The intrinsic contrast in standard anatomical MR images in the thalamus is very low, therefore direct segmentation of thalamic nuclei is not possible. Since thalamic nuclei have a distinct and well-ordered fiber str...

The method for nuclei segmentation in fluorescence in-situ hybridization (FISH) images, based on the inverse multifractal analysis (IMFA) is proposed. From the blue channel of the FISH image in RGB format, the matrix of Holder exponents, with one-by-one correspondence with the image pixels, is determined first. The following semi-automatic procedure is proposed: initial nuclei segmentation is p...