A Novel Multispace Image Reconstruction Method for Pathological Image Classification Based on Structural Information

Zhu, Honglin; Jiang, Huiyan; Li, Siqi; Li, Haoming; Pei, Yan

doi:10.1155/2019/3530903

Cited by 9 publications

(10 citation statements)

References 30 publications

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“…The authors later used the Lion optimized boosting support vector machine model for the classification of the tumor images. In pathological image classification, another recent work worth mentioning is the one by Zhu et al (2019). The GLCM was used here as a part of a multispace image reconstruction method and later applied for evaluation of a publicly available microscopy image dataset of malignant lymphoma (Zhu et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…In pathological image classification, another recent work worth mentioning is the one by Zhu et al (2019). The GLCM was used here as a part of a multispace image reconstruction method and later applied for evaluation of a publicly available microscopy image dataset of malignant lymphoma (Zhu et al, 2019). In the future, it is possible that a similar classification framework could later be developed for evaluation of the brain tissue.…”

Section: Discussionmentioning

confidence: 99%

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Gray-Level Co-Occurrence Matrix Analysis of Granule Neurons of the Hippocampal Dentate Gyrus Following Cortical Injury

et al. 2020

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Traumatic brain injury (TBI) is a main cause of death and disabilities in young adults. Although learning and memory impairments are a major clinical manifestation of TBI, the consequences of TBI on the hippocampus are still not well understood. In particular, how lesions to the sensorimotor cortex damage the hippocampus, to which it is not directly connected, is still elusive. Here, we study the effects of sensorimotor cortex ablation (SCA) on the hippocampal dentate gyrus, by applying a highly sensitive gray-level co-occurrence matrix (GLCM) analysis. Using GLCM analysis of granule neurons, we discovered, in our TBI paradigm, subtle changes in granule cell (GC) morphology, including textual uniformity, contrast, and variance, which is not detected by conventional microscopy. We conclude that sensorimotor cortex trauma leads to specific changes in the hippocampus that advance our understanding of the cellular underpinnings of cognitive impairments in TBI. Moreover, we identified GLCM analysis as a highly sensitive method to detect subtle changes in the GC layers that is expected to significantly improve further studies investigating the impact of TBI on hippocampal neuropathology.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Gray-Level Co-Occurrence Matrix Analysis of Granule Neurons of the Hippocampal Dentate Gyrus Following Cortical Injury

et al. 2020

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“…In [49,65,89,[98][99][100][101][102][103][104][105][106][107][108][109][110][111][112], L * a * b * color space is used on LHIA for L * a * b * allowing color changes to be compatible with differences in visual perception. In [49,110,113,114], L * u * v * color space is used on LHIA for L * u * v * color space are uniform in perception.…”

Section: Color-based Preprocessing Techniquesmentioning

confidence: 99%

What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review

Li¹,

Chen²,

Li³

et al. 2022

Preprint

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In the past ten years, the computing power of machine vision (MV) has been continuously improved, and image analysis algorithms have developed rapidly. At the same time, histopathological slices can be stored as digital images. Therefore, MV algorithms can provide doctors with diagnostic references. In particular, the continuous improvement of deep learning algorithms has further improved the accuracy of MV in disease detection and diagnosis. This paper reviews the applications of image processing technology based on MV in lymphoma histopathological images in recent years, including segmentation, classification and detection. Finally, the current methods are analyzed, some more potential methods are proposed, and further prospects are made.

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“…In our patch-level training, we use our data set to fine-tune the VGG-16 [ 6 ], which is pretrained on a large-scale image data set ImageNet [ 28 , 29 ].…”

Section: Multiscale Cnn-crf Modelmentioning

confidence: 99%

A Multiscale CNN‐CRF Framework for Environmental Microorganism Image Segmentation

Jing-hua

Kulwa

et al. 2020

BioMed Research International

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To assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multiscale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper. There are two parts in this framework: The first is a novel pixel-level segmentation approach, using a newly introduced Convolutional Neural Network (CNN), namely, “mU-Net-B3”, with a dense Conditional Random Field (CRF) postprocessing. The second is a VGG-16 based patch-level segmentation method with a novel “buffer” strategy, which further improves the segmentation quality of the details of the EMs. In the experiment, compared with the state-of-the-art methods on 420 EM images, the proposed MSCC method reduces the memory requirement from 355 MB to 103 MB, improves the overall evaluation indexes (Dice, Jaccard, Recall, Accuracy) from 85.24%, 77.42%, 82.27%, and 96.76% to 87.13%, 79.74%, 87.12%, and 96.91%, respectively, and reduces the volume overlap error from 22.58% to 20.26%. Therefore, the MSCC method shows great potential in the EM segmentation field.

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A Novel Multispace Image Reconstruction Method for Pathological Image Classification Based on Structural Information

Cited by 9 publications

References 30 publications

Gray-Level Co-Occurrence Matrix Analysis of Granule Neurons of the Hippocampal Dentate Gyrus Following Cortical Injury

Gray-Level Co-Occurrence Matrix Analysis of Granule Neurons of the Hippocampal Dentate Gyrus Following Cortical Injury

What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review

A Multiscale CNN‐CRF Framework for Environmental Microorganism Image Segmentation

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