Asymmetry analysis of breast thermograms using automated segmentation and texture features

Sathish, Dayakshini; Kamath, Surekha; Prasad, Keerthana; Kadavigere, Rajagopal; Martis, Roshan Joy

doi:10.1007/s11760-016-1018-y

Cited by 71 publications

(21 citation statements)

References 23 publications

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“…Breast thermogram images are segmented automatically to separate right and left breast for asymmetry analysis (Sathish et al, 2017). ROI of the breast is segmented from the background by applying Canny edge detection on the image to extract outer edges of the image.…”

Section: Methodsmentioning

confidence: 99%

Texture analysis of breast thermograms using neighbourhood grey tone difference matrix

Kadavigere¹,

Prasad²,

Sathish³

et al. 2018

IJBRA

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Breast cancer is the leading cancer in women worldwide. Early detection can reduce the mortality rate of breast cancer. Breast thermography is a non-invasive and simple imaging technique used for early detection of breast cancer. Feature extraction and selection of appropriate features play a major role in computer-aided detection of breast cancer using breast thermograms. In this article, texture features are extracted from automatically segmented breast thermograms by computing neighbourhood grey tone difference matrix (NGTDM) and run length matrix (RLM). Significance of these features in differentiating the abnormal breast from the normal breast is found by statistical test. NGTDM extracted coarseness, busyness, complexity, strength and RLM extracted long run emphasis and run percentage are found to be significant by statistical test. Extracted features are computationally less expensive and attained an average accuracy of 80%, sensitivity of 94% and specificity of 71.4% using back propagation neural network classifier.

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Section: Methodsmentioning

confidence: 99%

Texture analysis of breast thermograms using neighbourhood grey tone difference matrix

Kadavigere¹,

Prasad²,

Sathish³

et al. 2018

IJBRA

Self Cite

View full text Add to dashboard Cite

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“…Sathish et al [89] showed tracing shape edges by applying a polynomial curve fitting. This method was able to detect slight concavities in the upper border and convexities in the lower part.…”

Section: A Image Pre-processingmentioning

confidence: 99%

“…The resulting accuracy rates were 85%, 80%, and 92.5% for SVM, Naïve Bayes, and k-NN, respectively. A further study [89] classified normal and abnormal breast thermograms based on GLCM textural features and histograms. To improve the accuracy rate, SVM was tested with various kernel functions such as linear, radial basis function (RBF), polynomial, quadratic, and multilayer perceptron.…”

Section: Research On Breast Thermogram Classificationmentioning

confidence: 99%

A Review on Recent Progress in Thermal Imaging and Deep Learning Approaches for Breast Cancer Detection

et al. 2020

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Developing a breast cancer screening method is very important to facilitate early breast cancer detection and treatment. Building a screening method using medical imaging modality that does not cause body tissue damage (non-invasive) and does not involve physical touch is challenging. Thermography, a non-invasive and non-contact cancer screening method, can detect tumors at an early stage even under precancerous conditions by observing temperature distribution in both breasts. The thermograms obtained on thermography can be interpreted using deep learning models such as convolutional neural networks (CNNs). CNNs can automatically classify breast thermograms into categories such as normal and abnormal. Despite their demostrated utility, CNNs have not been widely used in breast thermogram classification. In this study, we aimed to summarize the current work and progress in breast cancer detection based on thermography and CNNs. We first discuss of breast thermography potential in early breast cancer detection, providing an overview of the availability of breast thermal datasets together with publicly accessible. We also discuss characteristics of breast thermograms and the differences between healthy and cancerous thermographic patterns. Breast thermogram classification using a CNN model is described step by step including a simulation example illustrating feature learning. We cover most research related to the implementation of deep neural networks for breast thermogram classification and propose future research directions for developing representative datasets, feeding the segmented image, assigning a good kernel, and building a lightweight CNN model to improve CNN performance. INDEX TERMS breast cancer; convolutional neural network; deep learning; early detection; thermogram

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“…This method used two types of projection, Horizontal Projection Profile (HPP) to detect the upper and lower borders, and Vertical Projection Profile (VPP) to detect the left and right borders. Authors in [44] used a new technique of segmentation after using HPP called asymmetry analysis which depends on finding the point of intersection to separate right and left breasts. In [45], the authors used a technique of segmentation called hot region segmentation method which depends on separating objects from background clearly after applying the technique of k-means clustering which was used to classify colors for Lab mode after the conversion from RGB mode to show the difference between colors and measure that difference.…”

Section: Related Workmentioning

confidence: 99%

Breast Cancer Segmentation From Thermal Images Based on Chaotic Salp Swarm Algorithm

et al. 2020

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Breast cancer is one of the most common types of cancer and early detection can significantly decrease the associated mortality rate. Different kinds of segmentation methods were applied to extract regions of interest from breast cancer images that are necessary to improve the classification. In this paper, a segmentation method for breast cancer from thermal images is introduced based on a proposed Chaotic Salp Swarm Algorithm (CSSA). Although the Salp Swarm Algorithm (SSA) shows superiority in singleobjective optimization problems, it suffers from a low convergence rate and local optima stagnation. In the proposed method, a segmentation algorithm is formulated using the quick-shift method for superpixels extraction whose parameters are optimized by CSSA. The quick-shift method generates compact and nearly uniform superpixels by clustering the breast thermal image pixels. CSSA algorithm is developed based on ten chaotic maps to enhance the original SSA convergence rate while accuracy could be improved by controlling the balance between exploration and exploitation. The proposed algorithm is applied to realworld thermal images for the breast area. The results demonstrate that the proposed CSSA algorithm achieves fast convergence for the unimodal benchmark functions and outperforms the original SSA algorithm. Moreover, a dataset from Mastology Research with Infrared Image (DMR-IR) is used to test the performance of the proposed algorithm. In experiments, the proposed optimized segmentation algorithm extracts the breast area from the background accurately where the region of interest is focused on the breast area and removes the unwanted area such as underarms and stomach which intern can enhance the results of cancer detection. Furthermore, the proposed algorithms achieve robustness for the segmentation of different healthy and unhealthy cases images compared to the state-of-the-art methods.

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Asymmetry analysis of breast thermograms using automated segmentation and texture features

Cited by 71 publications

References 23 publications

Texture analysis of breast thermograms using neighbourhood grey tone difference matrix

Texture analysis of breast thermograms using neighbourhood grey tone difference matrix

A Review on Recent Progress in Thermal Imaging and Deep Learning Approaches for Breast Cancer Detection

Breast Cancer Segmentation From Thermal Images Based on Chaotic Salp Swarm Algorithm

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