Mammogram Segmentation using Region based Method with Split and Merge Technique

Jothilakshmi, G. R.; Sharmila, P.; Raaza, Arun

doi:10.17485/ijst/2016/v9i40/99589

Cited by 10 publications

(5 citation statements)

References 5 publications

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“…Mrunalini et al [3] detail a method for categorizing and diagnosing plant diseases. In the Indian economy, it has been shown that a machine-learning-based recognition system can reduce costs and improve efficiency.…”

Section: Related Workmentioning

confidence: 99%

Soft Computing and Image Processing for Detection and Analysis of Plant Infections

A.¹

2022

MSEA

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We provide a comprehensive evaluation of methods for detecting plant diseases in images taken under normal lighting conditions. The aim of these methods is to use digital image processing to identify plant diseases, rank their severity, and classify them into different categories. Disease symptoms could appear everywhere on a plant, but researchers here focused on the parts of the plant that could be seen by the human eye, such the leaves and stems. This was done for two reasons: (a) to keep the essay at a manageable length, and (b) to provide a more in-depth explanation of the subtleties involved in dealing with roots, seeds, and fruits. Taking these factors into account was crucial to making this decision. There are three broad classes into which the consensus standards fall: detection, severity measurement, and classification. The algorithm's preliminary technical response serves as a basis for subsequent breakdown of each class. Experts in the fields of vegetable pathology and pattern recognition may find this study's comprehensive overview to be useful.

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Section: Related Workmentioning

confidence: 99%

Soft Computing and Image Processing for Detection and Analysis of Plant Infections

A.¹

2022

MSEA

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“…Then similar regions are merged to create the segmented result. The work [33] , used a blended approach of region-based method and splitting and merging technique. The proposed approach is applied on MIAS mammogram images.…”

Section: ) Thresholdingmentioning

confidence: 99%

Mammogram Segmentation Techniques: A Review

Justaniah¹,

Alhothali²,

Aldabbagh³

2021

IJACSA

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There is a significant development in computeraided detection (CADe) and computer-aided diagnostic (CADx) systems in recent years. This development coincides with the evolution of computing power and the growth of data. The CAD systems support detections and diagnosis of significant diseases, including cancer. Breast cancer is one of the most prevalent cancers influencing women and causing death around the world. Early detection of breast cancer has a significant effect on treatment. The typical CAD system goes through various steps, including image segmentation, feature extraction, and image classification. Image segmentation plays an important role in CAD systems and simplifies further processing. This review explores popular mammogram segmentation techniques. A mammogram is medical imaging which uses a low-dose x-ray system to see inner tissues of the breast. There are many segmentation techniques used to segment medical images. These techniques can be categorized into five main categories: regionbased methods, boundary-based methods, atlas-based methods, model-based methods, and deep learning. A ground truth image is needed to measure the performance of the segmentation algorithm. Different performance measurements were used to evaluate the segmentation process, including accuracy, precision, recall, F1 score, Hausdorff Distance, Jaccard, and Dice Index. The research in mammogram segmentation has yielded promising results, but there is room for improvements.

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“…Mammogram enhancement is necessary to highlight specific features of images. During segmentation, thresholding- [25], boundary- [26], and region-based [27] methods are used. Segmentation in image processing plays a central role in detection of the RoI Fig.…”

Section: Pattern Recognitionmentioning

confidence: 99%

Pattern Recognition and Size Prediction of Microcalcification Based on Physical Characteristics by Using Digital Mammogram Images

et al. 2018

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Breast cancer is one of the life-threatening cancers occurring in women. In recent years, from the surveys provided by various medical organizations, it has become clear that the mortality rate of females is increasing owing to the late detection of breast cancer. Therefore, an automated algorithm is needed to identify the early occurrence of microcalcification, which would assist radiologists and physicians in reducing the false predictions via image processing techniques. In this work, we propose a new algorithm to detect the pattern of a microcalcification by calculating its physical characteristics. The considered physical characteristics are the reflection coefficient and mass density of the binned digital mammogram image. The calculation of physical characteristics doubly confirms the presence of malignant microcalcification. Subsequently, by interpolating the physical characteristics via thresholding and mapping techniques, a three-dimensional (3D) projection of the region of interest (RoI) is obtained in terms of the distance in millimeter. The size of a microcalcification is determined using this 3D-projected view. This algorithm is verified with 100 abnormal mammogram images showing microcalcification and 10 normal mammogram images. In addition to the size calculation, the proposed algorithm acts as a good classifier that is used to classify the considered input image as normal or abnormal with the help of only two physical characteristics. This proposed algorithm exhibits a classification accuracy of 99%.

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Mammogram Segmentation using Region based Method with Split and Merge Technique

Cited by 10 publications

References 5 publications

Soft Computing and Image Processing for Detection and Analysis of Plant Infections

Soft Computing and Image Processing for Detection and Analysis of Plant Infections

Mammogram Segmentation Techniques: A Review

Pattern Recognition and Size Prediction of Microcalcification Based on Physical Characteristics by Using Digital Mammogram Images

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