Breast cancer detection using image enhancement and segmentation algorithms

Abdallah, Yousif Mohamed Y.; Elgak, Sami; Zain, Hosam; Rafiq, M.I.; Ebaid, Elabbas A.; Elnaema, Alaeldein A.

doi:10.4066/biomedicalresearch.29-18-1106

Cited by 17 publications

(5 citation statements)

References 12 publications

(16 reference statements)

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“…It is the most widely used agent for this purpose because of the advantages of 99mTcsestamibi tagging and its great efficiency in detecting carcinomas [56]. • Enhancement based upon wavelet transform and morphology, morphological operations (enhancement of image using multi-scale morphology) [59] • Direct contrast enhancement techniques [60] • Adaptive neighborhood contrast enhancement [61] • Removal of noise using wiener function [62] • An intuitionistic fuzzification scheme based on the optimization of intuitionistic fuzzy entropy and contrast limited adaptive histogram equalization (CLAHE) [63] • Mammogram enhancement, non-subsampled pyramid (NSP), low pass filter (LPF), high pass filter (HPF), directional filter bank (DFB), 2D-directional edge filter (HTDE), combining directional and scale features, adaptive histogram equalization (AHE), one-dimensional spatial profile of difference of Gaussian and HTDE filter, detection of microcalcification (MC) [64,65]…”

Section: Mammographymentioning

confidence: 99%

Digital Image Processing and Its Application for Medical Physics and Biomedical Engineering Area

Karmaker¹

2022

Digital Image Processing Applications

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The proper use of imaging modalities produces an image that aids in the detection of early stage abnormalities such as cancer, the identification of small precise lesions, and the presentation of internal illustration. A high-quality image can help doctors, radiologists, medical physicists, biomedical engineers, and scientists to make important decisions on ameliorate treatment planning that can reduce cancer mortality rates and provide life-saving results. This chapter outlines the features, attributes, and processing techniques of various medical imaging modalities utilized in the fields of radiation therapy and biomedical engineering. This study highlighted the significance of image processing in medical physics and biomedical engineering, characteristics of mammography, computed tomography (CT), ultrasound, magnetic resonance imaging (MRI), and positron emission tomography (PET) images. With their advanced application, various image processing approaches are distinguished. Images are collected through the journal, useful websites, the internet, or other sources. That can help teachers, students, researchers, scientists, and others comprehend and learn how to apply image processing techniques and which techniques will suit which modalities image. This chapter will provide a clear understanding of image processing techniques for medical physics and biomedical engineering participants, as well as an abundance of learning opportunities.

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Section: Mammographymentioning

confidence: 99%

Digital Image Processing and Its Application for Medical Physics and Biomedical Engineering Area

Karmaker¹

2022

Digital Image Processing Applications

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“…Thus, many traditional filters can be applied for noise removal, including a wavelet transform, median filter, mean filter, adaptive median filter, Gaussian filter, and adaptive Wiener filter [3,[110][111][112][113]. Furthermore, different traditional methods, such as histogram equalization (HE) [114,115], adaptive histogram equalization (AHE) [116], and contrast-limited adaptive histogram equalization (CLAHE) [117], can be used to enhance the image.…”

Section: Image Enhancementmentioning

confidence: 99%

Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

2020

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This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.

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“…The primary important features of the breast tumor represent a mass with a particular site, texture, shape, and border, each of these qualities is simple to analyze under the image processing methods by utilizing the MATLAB program. 6…”

Section: Mammographymentioning

confidence: 99%

“…The image processing performs a significant part to reduce the expense of the diagnosing process by improving the result of digital mammography. The primary important features of the breast tumor represent a mass with a particular site, texture, shape, and border, each of these qualities is simple to analyze under the image processing methods by utilizing the MATLAB program 6 …”

Section: Introductionmentioning

confidence: 99%

Classification of breast cancer mammogram images using convolution neural network

Albalawi

Manimurugan

Varatharajan

2020

Concurrency and Computation

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Summary Medical imaging systems have broadly used in the diagnosis and identification of breast cancer. It is essential to recognize breast tumor as soon as possible. Mammography is a widely utilized method for the identification of breast cancer. The identification of cancer is trailed by the segmentation of the cancer area in an image of the mammogram. Numerous researches have been made on the diagnosing and identification of breast cancer utilizing different classification and image processing methods. In this work, we proposed the Convolutional Neural Network (CNN) classifier for diagnosing breast cancer utilizing MIAS (Mammographic Image Analysis Society)‐dataset. CNN established as an efficient class of methods for image recognition problems. CNN is a deep learning system that extricates the feature of an image and utilizes those features for classification of the image. Because deep learning methods are utilized for high task objective Computer Vision, Medical Diagnosis, Image processing, and so on. Wiener filter is utilized to expel the noise and background of the image and the K‐means clustering technique was utilized for the segmentation. After segmentation, the features are extracted and classified utilizing CNN classifier. The performance of this proposed method was analyzed and compared with the conventional techniques based on accuracy, sensitivity, and specificity result parameters. From the comparison result, it is seen that the CNN classifier performed better compared with different techniques with 0.5‐4% additional accuracy and 3‐13% specificity.

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Breast cancer detection using image enhancement and segmentation algorithms

Cited by 17 publications

References 12 publications

Digital Image Processing and Its Application for Medical Physics and Biomedical Engineering Area

Digital Image Processing and Its Application for Medical Physics and Biomedical Engineering Area

Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

Classification of breast cancer mammogram images using convolution neural network

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