Abstract
The visual extraction of cellular, nuclear and tissue components from medical images is very vital in the diagnosis routine of different health related abnormalities and diseases. The objective of this work is to modify and efficiently combine different image processing methods supported by cascaded artificial neural networks in an automated system to perform segmentation analysis of medical microscopy images to extract nuclei located in either simple or complex clusters. The proposed system is applied on a publicly available data sets of microscopy nuclei cells. A GUI is designed and presented in this work to ease the analysis and screening of these images. The proposed system shows promising performance and reduced computational time cost. It is hoped that thus system and the corresponding GUI will construct platform base for several biomedical studies in the field of cellular imaging where further complex investigations and modelling of microscopy images could take place.
Highlights
Semantic image segmentation in fluorescence microscopy analysis refers to the separation process of cell components from the surrounding background by finding the boundaries of cellular, nuclear or histological structures with an adequate accuracy from images of stained tissues with different markers, Fig. 2B
Nuclear segmentation is an important step in the pipeline of many cytometry analyses because it forms the basis of many other operations and is often the first step in the overall cell segmentation [22]
In fluorescence labelled images of blood and bone marrow, high degrees of nuclei segmentation accuracy is reported by applying a classical image processing techniques such as shading correction and background followed by Otsu’s method and watershed algorithm based on inverse distance transform [23]
Summary
In fluorescence labelled images of blood and bone marrow, high degrees of nuclei segmentation accuracy is reported by applying a classical image processing techniques such as shading correction and background (grayscale opening) followed by Otsu’s method and watershed algorithm based on inverse distance transform [23]. In [24] a modified algorithm using the watershed algorithm based on morphological filtering operations is applied to choose the seeds of cell nuclei in tissue sections (i.e. foreground) and background as well. In this case, the merging of touching and overlapping regions is used to solve the over-seeded situations. In [24] method, it is required to manually choose and set specific values of certain parameters based on test images and use them on images of the same dataset or images taken under the same conditions
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