Recent Advances in Morphological Cell Image Analysis

Abstract

This paper summarizes the recent advances in image processing methods for morphological cell analysis. The topic of morphological analysis has received much attention with the increasing demands in both bioinformatics and biomedical applications. Among many factors that affect the diagnosis of a disease, morphological cell analysis and statistics have made great contributions to results and effects for a doctor. Morphological cell analysis finds the cellar shape, cellar regularity, classification, statistics, diagnosis, and so forth. In the last 20 years, about 1000 publications have reported the use of morphological cell analysis in biomedical research. Relevant solutions encompass a rather wide application area, such as cell clumps segmentation, morphological characteristics extraction, 3D reconstruction, abnormal cells identification, and statistical analysis. These reports are summarized in this paper to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed.

Neuromuscular disease classification system

“…Shape description and accurate segmentation is possible if the initial localization of the muscle fibers is known. 23 Therefore, in this paper the segmentation process is divided into two steps: identification of the muscle fiber localization by applying morphological operators, and accurate detection of the fiber contours by a watershed transformation. It is important to note that the segmentation method must be automatic and valid to all types of images [see Fig.…”

“…The morphological and structural characteristics of the whole biopsy constitute a vector, also called biosignature. 23 The objective of this section is to extract the features that correspond to the visual attributes defined by clinicians as particularly important for the mentioned pathology grading and diagnosis as well as a study of new features with inherent properties that escape from the evaluation of the pathologist and that could be useful for the characterization of these diseases. In this sense, both morphological and structural features are proposed.…”

“…23 Formulation of morphological features is an easy and fast way to automatize the manual morphological quantification, which is very laborious and subjective.…”

“…The kernel approach is to find a high correlation feature set without redundancy. 23 A principal component analysis is applied. Despite not having reliable values of the degrees of severity of biopsies with atrophic patterns, the PCA results for the six cases (the three comparisons with the sets of features of Table 8) are displayed in a bidimensional space representing the two first principal components (see Fig.…”

A complex network approach for nanoparticle agglomeration analysis in nanoscale images

“…We can find a rich literature with similar nanoparticle problems in biomedical imaging, such as detection and counting of cells [23], morphological cell classification [8], and cell tracking [40]. Unlike conventional cell image analysis, agglomeration and interaction analysis of nanoparticles are still a visual counting task.…”