scholarly journals Increased Mammogram Image Contrast Using Histogram Equalization And Gaussian In The Classification Of Breast Cancer

2020 ◽  
Vol 4 (01) ◽  
pp. 40-44
Author(s):  
Febri Liantoni ◽  
Coana Sukmagautama ◽  
Risalina Myrtha
Keyword(s):  
Breast Cancer ◽  
Contrast Enhancement ◽  
Nearest Neighbor ◽  
Region Of Interest ◽  
Histogram Equalization ◽  
Image Contrast ◽  
K Nearest Neighbor ◽  
Diagnose Breast Cancer ◽  
Image Contrast Enhancement ◽  
Mammogram Image

Breast cancer is one of the most common diseases among women in several countries. One of the most common methods to diagnose breast cancer is mammography. In this study, we propose a classification study to differentiate benign and malignant breast tumors based on mammogram image. The proposed system includes five major steps, i.e. preprocessing, histogram equalization, convolution, feature extraction, and classification. Image is cropped using region of interest (ROI) at preprocessing stage. In this study, we perform image contrast quality enhancement of the mammogram to view the breast cancer better. Image contrast enhancement uses histogram equalization and Gaussian filter. Gray-Level Co-Occurrence Matrix (GLCM) is used to extract the mammogram features. There are five features used i.e. entropy, correlation, contrast, homogeneity, and variance. The last step is to classify using naïve Bayes classifier (NBC) and k-nearest neighbor (KNN). Based on the hypothesis, the accuracy of NBC method is 90% and the accuracy of KKN method is 87.5%. So, the mammogram image contrast enhancement is well performed.

scholarly journals Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1870
Author(s):  
Yaghoub Pourasad ◽  
Esmaeil Zarouri ◽  
Mohammad Salemizadeh Parizi ◽  
Amin Salih Mohammed
Keyword(s):  
Breast Cancer ◽  
High Performance ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Ultrasound Images ◽  
Morphological Operations ◽  
K Nearest Neighbor ◽  
Diagnose Breast Cancer ◽  
Premature Deaths ◽  
Infected Area

Breast cancer is one of the main causes of death among women worldwide. Early detection of this disease helps reduce the number of premature deaths. This research aims to design a method for identifying and diagnosing breast tumors based on ultrasound images. For this purpose, six techniques have been performed to detect and segment ultrasound images. Features of images are extracted using the fractal method. Moreover, k-nearest neighbor, support vector machine, decision tree, and Naïve Bayes classification techniques are used to classify images. Then, the convolutional neural network (CNN) architecture is designed to classify breast cancer based on ultrasound images directly. The presented model obtains the accuracy of the training set to 99.8%. Regarding the test results, this diagnosis validation is associated with 88.5% sensitivity. Based on the findings of this study, it can be concluded that the proposed high-potential CNN algorithm can be used to diagnose breast cancer from ultrasound images. The second presented CNN model can identify the original location of the tumor. The results show 92% of the images in the high-performance region with an AUC above 0.6. The proposed model can identify the tumor’s location and volume by morphological operations as a post-processing algorithm. These findings can also be used to monitor patients and prevent the growth of the infected area.

Extraction of Multiple Diseases in Apple Leaf Using Machine Learning

2021 ◽  
pp. 2140009
Author(s):  
Swati Singh ◽  
Sheifali Gupta ◽  
Ankush Tanta ◽  
Rupesh Gupta
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Apple Scab ◽  
Region Of Interest ◽  
Texture Features ◽  
Histogram Equalization ◽  
K Nearest Neighbor ◽  
Plant Leaf ◽  
Diseased Part ◽  
Fuzzy Histogram

This paper proposes a novel algorithm of segmentation of diseased part in apple leaf images. In agriculture-based image processing, leaf diseases segmentation is the main processing task for region of interest extraction. It is also extremely important to segment the plant leaf from the background in case on live images. Automated segmentation of plant leaves from the background is a common challenge in the processing of plant images. Although numerous methods have been proposed, still it is tough to segment the diseased part of the leaf from the live leaf images accurately by one particular method. In the proposed work, leaves of apple having different background have been segmented. Firstly, the leaves have been enhanced by using Brightness-Preserving Dynamic Fuzzy Histogram Equalization technique and then the extraction of diseased apple leaf part is done using a novel extraction algorithm. Real-time plant leaf database is used to validate the proposed approach. The results of the proposed novel methodology give better results when compared to existing segmentation algorithms. From the segmented apple leaves, color and texture features are extracted which are further classified as marsonina coronaria or apple scab using different machine learning classifiers. Best accuracy of 96.4% is achieved using K nearest neighbor classifier.

scholarly journals Local Contrast Enhancement Utilizing Bidirectional Switching Equalization of Separated and Clipped Subhistograms

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Haidi Ibrahim ◽  
Seng Chun Hoo
Keyword(s):  
Contrast Enhancement ◽  
Histogram Equalization ◽  
Processing Technique ◽  
Image Contrast ◽  
Local Contrast ◽  
Image Contrast Enhancement ◽  
Enhancement Method ◽  
Equalization Technique ◽  
Histogram Bins ◽  
Almost All

Digital image contrast enhancement methods that are based on histogram equalization technique are still useful for the use in consumer electronic products due to their simple implementation. However, almost all the suggested enhancement methods are using global processing technique, which does not emphasize local contents. Therefore, this paper proposes a new local image contrast enhancement method, based on histogram equalization technique, which not only enhances the contrast, but also increases the sharpness of the image. Besides, this method is also able to preserve the mean brightness of the image. In order to limit the noise amplification, this newly proposed method utilizes local mean-separation, and clipped histogram bins methodologies. Based on nine test color images and the benchmark with other three histogram equalization based methods, the proposed technique shows the best overall performance.

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.

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