Texture‐based feature extraction of smear images for the detection of cervical cancer

Arya, Mithlesh; Mittal, Namita; Singh, Girdhari

doi:10.1049/iet-cvi.2018.5349

Cited by 43 publications

(13 citation statements)

References 39 publications

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“…Texture descriptors are used to describe the content of medical images 20 and histological images 21 , 22 . Some histological image studies are focused on cell 23 , 24 and in pathologies 25 – 27 . The proposed method in 14 is three-fold [see Fig.…”

Section: Methodsmentioning

confidence: 99%

Using an ontology of the human cardiovascular system to improve the classification of histological images

Mazo

Alegre

Trujillo

2020

Sci Rep

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The advantages of automatically recognition of fundamental tissues using computer vision techniques are well known, but one of its main limitations is that sometimes it is not possible to classify correctly an image because the visual information is insufficient or the descriptors extracted are not discriminative enough. An Ontology could solve in part this problem, because it gathers and makes possible to use the specific knowledge that allows detecting clear mistakes in the classification, occasionally simply by pointing out impossible configurations in that domain. One of the main contributions of this work is that we used a Histological Ontology to correct, and therefore improve the classification of histological images. First, we described small regions of images, denoted as blocks, using Local Binary Pattern (LBP) based descriptors and we determined which tissue of the cardiovascular system was present using a cascade Support Vector Machine (SVM). Later, we built Resource Description Framework (RDF) triples for the occurrences of each discriminant class. Based on that, we used a Histological Ontology to correct, among others, “not possible” situations, improving in this way the global accuracy in the blocks first and in tissues classification later. For the experimental validation, we used a set of 6000 blocks of pixels, obtaining F-Scores between 0.769 and 0.886. Thus, there is an improvement between 0.003 and when compared to the approach without the histological ontology. The methodology improves the automatic classification of histological images using a histological ontology. Another advantage of our proposal is that using the Ontology, we were capable of recognising the epithelial tissue, previously not detected by any of the computer vision methods used, including a CNN proposal called HistoResNet evaluated in the experiments. Finally, we also have created and made publicly available a dataset consisting of 6000 blocks of labelled histological tissues.

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Section: Methodsmentioning

confidence: 99%

Using an ontology of the human cardiovascular system to improve the classification of histological images

Mazo

Alegre

Trujillo

2020

Sci Rep

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“…However, the features can represent the nature of the region. The textures can be extracted by gray level cooccurrence matrix (GLCM) that adapts to the characteristics of human visuality [32], [33].…”

Section: The Shape and Grayscale Features Of The Defectsmentioning

confidence: 99%

Particle Swarm Optimization-Based SVM for Classification of Cable Surface Defects of the Cable-Stayed Bridges

Guo

2020

IEEE Access

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The machine vision system was employed to inspect the surface defects of bridge cables of cable-stayed bridges. After the acquisition and preprocessing of the defect images, it is necessary to classify and identify the defects of the cables to meet the requirements of non-destructive testing and evaluation. In this paper, feature extraction for defect images was performed using mathematical statistical methods. After that, 10 feature parameters including shape features, grayscale features and texture features of the defect images were obtained and selected for a classification model of support vector machine (SVM). To improve the SVM classification performance, the particle swarm optimization algorithm (PSO) was adopted to obtain the punish factor c and the kernel parameter g of the SVM model, namely the PSO-SVM algorithm. Finally, our PSO-SVM classification model was employed to implement the classification of real surface defect images of the bridge cables. Longitudinal crack, transverse crack, surface corrosion, and pothole defect can be automatically identified and the classification accuracy reached 96.25%. The experimental results showed that the PSO-SVM model can improve the classification performance of the surface defects. Based on the effective classification, we can find the distribution characteristics of the surface defects of the cable. It is very important to analyze the relationship between the type of surface defects and the material of the protective layer, so as to adopt appropriate materials and reasonable maintenance measures. Thus, it has a great significance in the structural health monitoring of bridge cables.

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“…These varying features are identified and should be used in the classification of cell. The various features usually used are  multiple texture based feature methods  color, texture, shape  cytoplasm and nucleus-cytoplasm ratio  mean, variance, skewness  energy, entropy Mithlesh Arya et al (2018), developed an automated system for the detection of cervical cancer [5]. Histogram, grey level co-occurrence matrix (GLCM), local binary pattern (LBP), laws textural energy measures, DWT are used for the extraction of texture feature.…”

Section: Iiifeature Extractionmentioning

confidence: 99%

Automated Detection of Cervical Cancer

Devi¹,

Thirumurugan²

2019

IJITEE

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Cervical Cancer is an abnormal growth in the cervix-the lower part of the uterus which joins to the vagina. Human Papilloma Virus (HPV) is identified as the main cause for cervical cancer. Cervical cancer is the second most deadly disease among women next to breast cancer in developing countries. However, it is considered to be the most preventable female cancer if identified at an early stage. The cancerous cells may spread to other parts of the body if not identified at an early stage. Pap Smear test and acetic acid test are usually done for cancer screening. In pap test cells are taken from the vagina and cervix and are examined under a microscope for the presence of an abnormal cell. In acetic acid test, the change in features after and before the application of acetic acid is analyzed to find the existence of abnormal cell. Automated screening is becoming most common than manual screening because the latter is erroneous. This paper surveys the different automated methods available for screening the abnormal cells in pap images.

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Texture‐based feature extraction of smear images for the detection of cervical cancer

Cited by 43 publications

References 39 publications

Using an ontology of the human cardiovascular system to improve the classification of histological images

Using an ontology of the human cardiovascular system to improve the classification of histological images

Particle Swarm Optimization-Based SVM for Classification of Cable Surface Defects of the Cable-Stayed Bridges

Automated Detection of Cervical Cancer

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