scholarly journals Using Morphological Operation and Watershed Techniques for Breast Cancer Detection

2020 ◽  
Vol 16 (05) ◽  
pp. 140
Author(s):  
Sarah Faris Ameer ◽  
Zinah Tareq Nayyef ◽  
Zena Hussain Fahad ◽  
Ibtihal Razaq Niama ALRubee
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Cancer Detection ◽  
Median Filter ◽  
Diagnose Breast Cancer ◽  
Causes Of Mortality ◽  
Mammogram Image ◽  
Effective Diagnosis ◽  
Reliable Methods ◽  
Mammogram Images

<pre>Breast cancer is one of the leading causes of mortality between women, with one in eight women diagnosed with the disease, but early detection can reduce death rates. Therefore, continuous effort is being made to advance more effective methods for the early and effective diagnosis of breast cancer with high accuracy without human intervention. Classical attempts were manual, time- consuming and ineffective in many situations. The purpose of this work is to detect and locate the presence of malignant tissues in the breast using the morphological technique in mammogram images to diagnose breast cancer because morphology is one of the most reliable methods for early detection of breast cancer. The proposed algorithm is developed using watershed segmentation after the preprocessing is completed by the median filter to eliminate any expected noise, and contouring the tumor by morphological techniques to take the best diagnostic for breast cancer in a mammogram image. Good results are obtained for the measurements used like MSE, PSNR, SNR, entropy for the mammogram images.    </pre>

scholarly journals Assessment of Breast Cancer Detection and Implementation

2019 ◽  
Vol 8 (11) ◽  
pp. 323-330
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Median Filter ◽  
Breast Cancer Detection ◽  
Statistical Parameters ◽  
Cause Of Deaths ◽  
Life Threatening ◽  
Mammogram Image ◽  
Mean Variance ◽  
Progressive Algorithms

Breast Cancer is the most dangerous and life threatening disease. Of all kinds of cancers, Breast cancer is the second major cause of deaths and especially the first major cause of deaths in women. In this project, images are taken from medical representativess in order to implement a real time project. This methodology aims at diagnosing Breast Cancer at an earlier stage by considering progressive algorithms. In this methodology, a mammogram image is considered. To this image sample, image segmentation technique is applied which separates fore-ground regions from the background regions. Later, Binarization technique is used to enrich the contrast of the image in order to make it more desirable for finding the tumour cell location within the affected area. Median filter is used for removing noise within the image. To the noise free images, some statistical parameters viz., mean, variance, Standard deviation, Mean Square error and entropy are calculated to analyze the performance

An Optimized Approach for Breast Cancer Classification for Histopathological Images Based on Hybrid Feature Set

2020 ◽  
Vol 16 ◽  
Author(s):  
Inzamam Mashood Nasir ◽  
Muhammad Rashid ◽  
Jamal Hussain Shah ◽  
Muhammad Sharif ◽  
Muhammad Yahiya Haider Awan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Classification Performance ◽  
Diagnose Breast Cancer ◽  
Histopathological Images ◽  
And Performance ◽  
Learned Features ◽  
Intelligent Healthcare

Background: Breast cancer is considered as the most perilous sickness among females worldwide and the ratio of new cases is expanding yearly. Many researchers have proposed efficient algorithms to diagnose breast cancer at early stages, which have increased the efficiency and performance by utilizing the learned features of gold standard histopathological images. Objective: Most of these systems have either used traditional handcrafted features or deep features which had a lot of noise and redundancy, which ultimately decrease the performance of the system. Methods: A hybrid approach is proposed by fusing and optimizing the properties of handcrafted and deep features to classify the breast cancer images. HOG and LBP features are serially fused with pretrained models VGG19 and InceptionV3. PCR and ICR are used to evaluate the classification performance of proposed method. Results: The method concentrates on histopathological images to classify the breast cancer. The performance is compared with state-of-the-art techniques, where an overall patient-level accuracy of 97.2% and image-level accuracy of 96.7% is recorded. Conclusion: The proposed hybrid method achieves the best performance as compared to previous methods and it can be used for the intelligent healthcare systems and early breast cancer detection.

A Survey on Early Detection of Women's Breast Cancer Using IoT

2021 ◽  
pp. 208-219
Author(s):  
P. Malathi ◽  
A. Kalaivani
Keyword(s):  
Breast Cancer ◽  
Internet Of Things ◽  
Early Detection ◽  
Time Data ◽  
Diagnose Breast Cancer ◽  
Current State ◽  
Past Data ◽  
Self Examination ◽  
The Internet Of Things ◽  
Detect Breast Cancer

The internet of things is probably one of the most challenging and disruptive concepts raised in recent years. Recent development in innovation and availability have prompted the rise of internet of things (IoT). IoT technology is used in a wide scope of certified application circumstances. Internet of things has witnessed the transition in life for the last few years which provides a way to analyze both the real-time data and past data by the emerging role. The current state-of-the-art method does not effectively diagnose breast cancer in the early stages. Thus, the early detection of breast cancer poses a great challenge for medical experts and researchers. This chapter alleviates this by developing a novel software to detect breast cancer at a much earlier stage than traditional methods or self-examination.

Potential Use of Biomarkers to Augment Clinical Decisions for the Early Detection of Breast Cancer

2014 ◽  
Vol 10 (02) ◽  
pp. 103 ◽  
Author(s):  
Alan B Hollingsworth ◽  
David E Reese ◽  
◽  
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Survival Rates ◽  
High Sensitivity ◽  
Standard Of Care ◽  
Serum Biomarkers ◽  
Breast Cancer Detection ◽  
Breast Cancers

Breast cancer remains a significant worldwide health problem, despite the fact that early detection is associated with excellent survival rates. Currently, a substantial proportion of breast cancers are not detected using routine screening. Therefore, there is a need to identify a technology that can improve the precision and accuracy of early breast cancer detection. Biomarkers are attractive in that they can potentially detect early cancers with high sensitivity, while distinguishing between benign disease and invasive cancers. Many commonly used serum biomarkers have limited use in screening assays for breast cancer as single agents due to the heterogeneous nature of breast cancer. However, the use of protein panels that detect multiple serum biomarkers offer the potential for enhanced sensitivity and specificity in a clinical setting. Recently, a serum biomarker test comprising five serum biomarkers for breast cancer was clinically validated and showed high sensitivity and specificity. Additional panels have been developed that combine serum protein biomarkers (SPB) and tumor-associated autoantibodies (TAb) to further enhance the clinical utility of the assay. Serum biomarkers are currently not the standard of care and are not recommended in any detection guidelines. However, tumor biomarkers are used in the breast cancer setting to determine the course of care. The purpose of this article is to review recent advances in SPB, TAb, and biomarkers used in breast cancer detection to provide a perspective on how these technologies may offer benefit when combined with current imaging modalities.

Feasibility of New-generation Infrared Imaging Screening for Breast Cancer in Rural Communities

2010 ◽  
Vol 06 ◽  
pp. 60
Author(s):  
Francisco Gutierrez-Delgado ◽  
José Guadalupe Vázquez-Luna ◽  
◽  
Keyword(s):  
Breast Cancer ◽  
Rural Communities ◽  
Early Detection ◽  
Early Breast Cancer ◽  
Cancer Detection ◽  
Infrared Imaging ◽  
Breast Cancer Detection ◽  
Southern Mexico ◽  
Breast Thermography ◽  
New Generation

Breast cancer is a major public health problem worldwide. Important advances have improved survival, but early detection remains the main clinical challenge in reducing mortality. Currently, mammography is the ‘gold standard’ tool for breast cancer screening. However, the search for an early breast cancer detection method is the subject of extensive research. Although infrared imaging or breast thermography for early breast cancer detection has been evaluated since the late 1950s, the negative results reported in 1979 by the Breast Cancer Detection and Demonstration Project decreased interest in this imaging modality. Advances in infrared imaging and reduced equipment costs have, however, renewed interest in breast thermography. Breast cancer in developing countries requires new strategies to increase early detection and access to care. In this article, we highlight the principles and advances of infrared imaging technology and describe our experience with new-generation infrared imaging for early breast cancer detection in rural communities in southern Mexico.

DIGITAL MAMMOGRAM ENHANCEMENT USING KAPUR MEASURE OF ENTROPY AND MATHEMATICAL MORPHOLOGY

2013 ◽  
Vol 25 (03) ◽  
pp. 1350029 ◽  
Author(s):  
Baljit Singh Khehra ◽  
Amar Partap Singh Pharwaha
Keyword(s):  
Breast Cancer ◽  
Image Analysis ◽  
Early Detection ◽  
Contrast Enhancement ◽  
Mathematical Morphology ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Signal To Noise ◽  
Mammogram Image ◽  
Background Contrast

Mammography is the most reliable, effective, low cost and highly sensitive method for early detection of breast cancer. Mammogram analysis usually refers to the processing of mammograms with the goal of finding abnormality presented in the mammogram. Mammogram enhancement is one of the most critical tasks in automatic mammogram image analysis. Main purpose of mammogram enhancement is to enhance the contrast of details and subtle features while suppressing the background heavily. In this paper, a hybrid approach is proposed to enhance the contrast of microcalcifications while suppressing the background heavily, using fuzzy logic and mathematical morphology. First, mammogram is fuzzified using Gaussian fuzzy membership function whose bandwidth is computed using Kapur measure of entropy. After this, mathematical morphology is applied on fuzzified mammogram. Mathematical morphology provides tools for the extraction of microcalcifications even if the microcalcifications are located on a nonuniform background. Main advantage of Kapur measure of entropy over Shannon entropy is that Kapur measure of entropy has α and β parameters that can be used as adjustable values. These parameters can play an important role as tuning parameters in the image processing chain for the same class of images. Experiments have been conducted on images of mini-Mammogram Image Analysis Society (MIAS) database (UK). Experiment results of the proposed approach are compared with histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE) and fuzzy histogram hyperbolization (FHH) which are well-established image enhancement techniques. In order to validate the results, several different kinds of standard test images (fatty, fatty-glandular and dense-glandular) of mini-MIAS database are considered. Objective image quality assessment parameters: Target-to-background contrast enhancement measurement based on standard deviation (TBCSD), target-to-background contrast enhancement measurement based on entropy (TBCE), contrast improvement index (CII), peak signal-to-noise ratio (PSNR) and average signal-to-noise ratio (ASNR) are used to evaluate the performance of proposed approach. The experimental results show that the proposed approach performs well. This study can be a part of developing a computer-aided diagnosis (CAD) system for early detection of breast cancer.

HYBRID REGISTRATION OF CORRESPONDING MAMMOGRAM IMAGES FOR AUTOMATIC DETECTION OF BREAST CANCER

2007 ◽  
Vol 19 (06) ◽  
pp. 359-374 ◽  
Author(s):  
Yih-Chih Chiou ◽  
Chern-Sheng Lin ◽  
Cheng-Yu Lin
Keyword(s):  
Breast Cancer ◽  
Image Registration ◽  
Early Detection ◽  
Feature Matching ◽  
Automatic Detection ◽  
Spatial Transformation ◽  
Registration Method ◽  
Feature Based ◽  
Mammogram Images ◽  
Hybrid Registration

Mammogram registration is a critical step in automatic detection of breast cancer. Much research has been devoted to registering mammograms using either feature-matching or similarity measure. However, a few studies have been done on combining these two methods. In this research, a hybrid mammogram registration method for the early detection of breast cancer is developed by combining feature-based and intensity-based image registration techniques. Besides, internal and external features were used simultaneously during the registration to obtain a global spatial transformation. The experimental results indicates that the similarity between the two mammograms increases significantly after a proper registration using the proposed TPS-registration procedures.

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