scholarly journals Multi-Resolution Wavelet-Transformed Image Analysis of Histological Sections of Breast Carcinomas

2005 ◽  
Vol 27 (4) ◽  
pp. 237-244 ◽  
Author(s):  
Hae-Gil Hwang ◽  
Hyun-Ju Choi ◽  
Byeong-Il Lee ◽  
Hye-Kyoung Yoon ◽  
Sang-Hee Nam ◽  
...  
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Discriminant Analysis ◽  
Visual Analysis ◽  
Ductal Carcinoma ◽  
Texture Features ◽  
Cancer Tissue ◽  
Histological Sections ◽  
Haar Transform ◽  
Ductal Hyperplasia

Multi-resolution images of histological sections of breast cancer tissue were analyzed using texture features of Haar- and Daubechies transform wavelets. Tissue samples analyzed were from ductal regions of the breast and included benign ductal hyperplasia, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (CA). To assess the correlation between computerized image analysis and visual analysis by a pathologist, we created a two-step classification system based on feature extraction and classification. In the feature extraction step, we extracted texture features from wavelet-transformed images at 10× magnification. In the classification step, we applied two types of classifiers to the extracted features, namely a statistics-based multivariate (discriminant) analysis and a neural network. Using features from second-level Haar transform wavelet images in combination with discriminant analysis, we obtained classification accuracies of 96.67 and 87.78% for the training and testing set (90 images each), respectively. We conclude that the best classifier of carcinomas in histological sections of breast tissue are the texture features from the second-level Haar transform wavelet images used in a discriminant function.

scholarly journals Sharp Downregulation of Hub Genes Associated With the Pathogenesis of Breast Cancer From Ductal Carcinoma In Situ to Invasive Ductal Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Yao Wang ◽  
Faqing Liang ◽  
Yuting Zhou ◽  
Juanjuan Qiu ◽  
Qing Lv ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Protein Expression ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Cancer Tissue ◽  
Hub Genes ◽  
Expression Levels ◽  
Cancer Pathogenesis ◽  
Ductal Hyperplasia

IntroductionBreast atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS) are precursor stages of invasive ductal carcinoma (IDC). This study aimed to investigate the pathogenesis of breast cancer by dynamically analyzing expression changes of hub genes from normal mammary epithelium (NME) to simple ductal hyperplasia (SH), ADH, DCIS, and finally to IDC.MethodsLaser-capture microdissection (LCM) data for NME, SH, ADH, DCIS, and IDC cells were obtained. Weighted gene co-expression network analysis (WGCNA) was performed to dynamically analyze the gene modules and hub genes associated with the pathogenesis of breast cancer. Tissue microarray, immunohistochemical, and western blot analyses were performed to determine the protein expression trends of hub genes.ResultsTwo modules showed a trend of increasing expression during the development of breast disease from NME to DCIS, whereas a third module displayed a completely different trend. Interestingly, the three modules displayed inverse trends from DCIS to IDC compared with from NME to DCIS; that is, previously upregulated modules were subsequently downregulated and vice versa. We further analyzed the module that was most closely associated with DCIS (p=7e−07). Kyoto Gene and Genomic Gene Encyclopedia enrichment analysis revealed that the genes in this module were closely related to the cell cycle (p= 4.3e–12). WGCNA revealed eight hub genes in the module, namely, CDK1, NUSAP1, CEP55, TOP2A, MELK, PBK, RRM2, and MAD2L1. Subsequent analysis of these hub genes revealed that their expression levels were lower in IDC tissues than in DCIS tissues, consistent with the expression trend of the module. The protein expression levels of five of the hub genes gradually increased from NME to DCIS and then decreased in IDC. Survival analysis predicted poor survival among breast cancer patients if these hub genes were not downregulated from DCIS to IDC.ConclusionsFive hub genes, RRM2, TOP2A, PBK, MELK, and NUSAP1, which are associated with breast cancer pathogenesis, are gradually upregulated from NME to DCIS and then downregulated in IDC. If these hub genes are not downregulated from DCIS to IDC, patient survival is compromised. However, the underlying mechanisms warrant further elucidation in future studies.

scholarly journals Machine Learning-Based Image Analysis for Accelerating the Diagnosis of Complicated Preneoplastic and Neoplastic Ductal Lesions in Breast Biopsy Tissues

2021 ◽  
Author(s):  
Shinya Sato ◽  
Satoshi Maki ◽  
Takashi Yamanaka ◽  
Daisuke Hoshino ◽  
Yukihide Ota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Breast Biopsy ◽  
Ductal Carcinoma ◽  
Pathological Diagnosis ◽  
Comparison Analysis ◽  
Preneoplastic Lesions ◽  
Biopsy Specimens ◽  
Ductal Hyperplasia ◽  
Neoplastic Lesions

Abstract Purpose: Diagnosis of breast preneoplastic and neoplastic lesions is difficult due to their similar morphology in breast biopsy specimens. To diagnose these lesions, pathologists perform immunohistochemical analysis and consult with expert breast pathologists. These additional examinations are time-consuming and expensive. Artificial intelligence (AI)-based image analysis has recently improved, and may help in ordinal pathological diagnosis. Here, we showed the significance of machine learning-based image analysis of breast preneoplastic and neoplastic lesions for facilitating high-throughput diagnosis.Methods: Images were obtained from normal mammary glands, hyperplastic lesions, preneoplastic lesions and neoplastic lesions, such as usual ductal hyperplasia (UDH), columnar cell lesion (CCL), ductal carcinoma in situ (DCIS), and DCIS with comedo necrosis (comedo DCIS) in breast biopsy specimens. The original enhanced convoluted neural network (CNN) system was used for analyzing the pathological images. Results: The AI-based image analysis provided the following area under the curve values (AUC): normal lesion vs. DCIS, 0.9902; DCIS vs. comedo DCIS, 0.9942; normal lesion vs. CCL, 0.9786; and UDH vs. DCIS, 1.000. Multiple comparison analysis showed precision and recall scores similar to those of single comparison analysis. Based on the Gradient-weighted Class Activation Mapping (Grad-CAM) used to visualize the important regions reflecting the result of CNN analysis, the ratio of stromal tissue in the whole weighted area was significantly higher in UDH and CCL than that in DCIS. Conclusions: These analyses may provide a more accurate and rapid pathological diagnosis of patients. Moreover, Grad-CAM identifies uncharted important histological characteristics for newer pathological findings and targets of research for understanding diseases.

A Review on Feature Extraction Methods for Image Analysis

2020 ◽  
Author(s):  
Vricha Chavan ◽  
​Jimit Shah ◽  
Mrugank Vora ◽  
Mrudula Vora ◽  
Shubhashini Verma
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Extraction Methods

scholarly journals Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

2021 ◽  
Vol 7 (3) ◽  
pp. 51
Author(s):  
Emanuela Paladini ◽  
Edoardo Vantaggiato ◽  
Fares Bougourzi ◽  
Cosimo Distante ◽  
Abdenour Hadid ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Digital Pathology ◽  
Texture Features ◽  
Tissue Type ◽  
Cancer Tissue ◽  
Learning Methods ◽  
Feature Based ◽  
Type Classification ◽  
Whole Slide Images

In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multi-classes CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161). Moreover, we propose two Ensemble CNN approaches: Mean-Ensemble-CNN and NN-Ensemble-CNN. The experimental results show that the proposed approaches outperformed the hand-crafted feature-based methods, CNN architectures and the state-of-the-art methods in both databases.

scholarly journals A Feature Set for Cytometry on Digitized Microscopic Images

2003 ◽  
Vol 25 (1) ◽  
pp. 1-36 ◽  
Author(s):  
Karsten Rodenacker ◽  
Ewert Bengtsson
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Systematic Approach ◽  
Structural Features ◽  
Biomedical Image Analysis ◽  
Feature Sets ◽  
Biomedical Image ◽  
Rules Of Thumb ◽  
Quantitative Cytology ◽  
The Eu

Feature extraction is a crucial step in most cytometry studies. In this paper a systematic approach to feature extraction is presented. The feature sets that have been developed and used for quantitative cytology at the Laboratory for Biomedical Image Analysis of the GSF as well as at the Center for Image Analysis in Uppsala over the last 25 years are described and illustrated. The feature sets described are divided into morphometric, densitometric, textural and structural features. The latter group is used to describe the eu‐ and hetero‐chromatin in a way complementing the textural methods. The main goal of the paper is to bring attention to the need of a common and well defined description of features used in cyto‐ and histometrical studies. The application of the sets of features is shown in an overview of projects from different fields. Finally some rules of thumb for the design of studies in this field are proposed. Colour figures can be viewed onhttp://www.esacp.org/acp/2003/25‐1/rodenacker.htm.

Medical image analysis of 3D CT images based on extension of Haralick texture features

2008 ◽  
Vol 32 (6) ◽  
pp. 513-520 ◽  
Author(s):  
Ludvík Tesař ◽  
Akinobu Shimizu ◽  
Daniel Smutek ◽  
Hidefumi Kobatake ◽  
Shigeru Nawano
Keyword(s):  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Texture Features ◽  
Ct Images ◽  
3D Ct

scholarly journals Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 270-279
Author(s):  
Quanbao Li ◽  
Fajie Wei ◽  
Shenghan Zhou
Keyword(s):  
Feature Extraction ◽  
Data Structure ◽  
Discriminant Analysis ◽  
Ad Hoc ◽  
Adaptive Method ◽  
Local Data ◽  
Linear Discriminant ◽  
Linear Feature ◽  
Nonparametric Discriminant Analysis ◽  
Nonlinear Feature Extraction

AbstractThe linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.

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