Segmentation and 3D Reconstruction of Animal Tissues in Histological Images

Azevedo, Liliana; Faustino, Augusto; Tavares, João Manuel R. S.

doi:10.1007/978-3-319-15799-3_14

Cited by 6 publications

(2 citation statements)

References 18 publications

(24 reference statements)

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“…In this research, normalization is performed on the histopathological image for enhancing or de-noising the images. Most of the histopathological images are collected or captured from the equipment's, whereas the collected histopathological images majorly consist of two noises: impulse noise and machinery noise (electrical and mechanical noises) [1]. The normalization methodology is very effective to remove impulse and machinery noise and also helps to enhance the image quality significantly.…”

Section: Pre-processing Of Histopathological Imagementioning

confidence: 99%

Histopathological Image Segmentation Using Modified Kernel-Based Fuzzy C-Means and Edge Bridge and Fill Technique

Karobari¹,

Suresh

2019

Journal of Intelligent Systems

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Abstract Histopathological lung cancer segmentation using region of interest is one of the emerging research area in the field of health monitoring system. In this paper, the histopathological images were collected from the database Stanford Tissue Microarray Database (TMAD). After image collection, pre-processing was performed using a normalization technique, which enhances the quality of the histopathological image by eliminating unwanted noise. After pre-processing, segmentation was carried out using the modified kernel-based fuzzy c-means clustering (KFCM) approach along with the edge bridge and fill technique (EBFT). It was a flexible high-level machine learning technique to localize the object in a complex template. The experimental result shows that the proposed approach segments the normal and abnormal cancer regions by means of precision, recall, specificity, accuracy, and Jaccard coefficient. The proposed methodology improved the classification accuracy in lung cancer segmentation up to 2.5–5% compared to the existing methods deep convolutional neural network (DCNN) and diffusion-weighted approach.

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Section: Pre-processing Of Histopathological Imagementioning

confidence: 99%

Histopathological Image Segmentation Using Modified Kernel-Based Fuzzy C-Means and Edge Bridge and Fill Technique

Karobari¹,

Suresh

2019

Journal of Intelligent Systems

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“…Thus, staining with DAPI, which is characterized by a peak excitation/emission spectrum of 358 nm/461 nm, can be used as a tool to identify signals. While several histopathological image analysis methods exist in the literature, to the best of our knowledge, there is no work that uses multiplexed guided images with anti‐PTBP1 antibody and 4',6‐diamidino‐2‐phenylindole (DAPI).…”

Section: Introductionmentioning

confidence: 99%

Quantitative validation of anti‐PTBP1antibody for diagnostic neuropathology use: Image analysis approach

Göçeri

Goksel

Elder

et al. 2017

Numer Methods Biomed Eng

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Problem Diagnostic neuropathology traditionally relies on subjective interpretation of visual data obtained from a brightfield microscope. This results in high variability, inability to multiplex, and unsatisfactory reproducibility even among experts. These diagnostic problems may affect patient outcomes and confound clinical decision-making. Furthermore, standard histological processing of pathological specimens results in auto-fluorescence and other artifacts, which have nearly blocked the implementation of fluorescent microscopy in diagnostic pathology. Thus, generation of objective and quantitative methodologies would augment the toolbox available to neuropathologists, which would ultimately enhance clinical decision making. Objective To develop image analysis methods to quantitatively validate anti-PTBP1 antibody for use in diagnostic neuropathology. Method We propose a computerized image analysis method to validate anti-PTBP1 antibody. Images were obtained from standard neuropathological specimens stained with anti-PTBP1 antibody. First, the noise characteristics of the images were modeled and images are de-noised according to the noise model. Next, images are filtered with sigma-adaptive Gaussian filtering for local normalization, and cell nuclei are detected and segmented with a k-means based deterministic approach. Result Experiments on 29 data sets from three cases of brain tumor (recurrent glioma, primary resections of glioblastoma harboring the EGFRVIII mutation, pilocytic astrocytoma) and reactive gliosis show statistically significant differences between the number of positively stained nuclei in images stained with and without anti-PTBP1 antibody (p values, t-test, are 40×10−4, 33×10−4, 6×10−4 and 46×10−3, respectively). Conclusion The experimental of analysis of specimens from three different brain tumor groups and one reactive gliosis group indicate the feasibility of using anti-PTBP1 antibody in diagnostic neuropathology and computerized image analysis provides a systematic and quantitative approach to explore feasibility.

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Analysis of Topological Variability in Mouse Neuronal Populations Based on Fluorescence Microscopy Images

Zaleshina

Zaleshin

2021

Communications in Computer and Information Science

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Segmentation and 3D Reconstruction of Animal Tissues in Histological Images

Cited by 6 publications

References 18 publications

Histopathological Image Segmentation Using Modified Kernel-Based Fuzzy C-Means and Edge Bridge and Fill Technique

Histopathological Image Segmentation Using Modified Kernel-Based Fuzzy C-Means and Edge Bridge and Fill Technique

Quantitative validation of anti‐PTBP1antibody for diagnostic neuropathology use: Image analysis approach

Analysis of Topological Variability in Mouse Neuronal Populations Based on Fluorescence Microscopy Images

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