scholarly journals An Introspective Performance Analysis of Threshold-based Segmentation Techniques on Digital Mammograms

YMER Digital ◽  
2021 ◽  
Vol 20 (11) ◽  
pp. 176-195
Author(s):  
A Nithya ◽  
◽  
P Shanmugavadivu ◽  
Keyword(s):  
Image Segmentation ◽  
Medical Image Analysis ◽  
Region Of Interest ◽  
Computation Time ◽  
Vital Role ◽  
Cancer Prognosis ◽  
Processing Step ◽  
Segmentation Methods ◽  
Reliable Source ◽  
Mammogram Images

Image segmentation, as a pre-processing step, plays a vital role in medical image analysis. The variants of threshold-based image segmentation methods are proved to offer feasible and optimal solutions to extract the region of interest (RoI), from medical images. Digital mammograms are used as a reliable source of breast cancer prognosis and diagnosis. Thresholding is a simple and effective strategy that finds applications in image processing and analysis. This research aimed to analyze the performance behaviour of a few threshold-based segmentation methods with respect to the intensity distribution of the input mammograms. For this analytical research, six automated thresholding segmentation techniques were chosen: Kapur, Otsu’s, Isoentropic, Ridler & Calvard’s, Kittler & Illingworth's, and Yen. The performance and behaviour of those methods were validated on the digital mammogram images of mini-MIAS database featured with Fatty (F), Fatty-Glandular (G), and Dense-Glandular (D) breast tissues. Those methods were analyzed on two metrics viz., Region Non-Uniformity (RNU) and computation time. The results of this research confirm that Ridler & Calvard’s method gives the best segmentation results for Dense-Glandular, Isoentropic method gives better segmentation results for Fatty and Yen method works well on the Fatty-Glandular normal mammogram images.

scholarly journals Breast Cancer Detection Using Combination of Feature Extraction Models

2018 ◽  
Vol 7 (3.12) ◽  
pp. 848
Author(s):  
T Suneetha Rani ◽  
S J Soujanya ◽  
Pole Anjaiah
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Cancer Detection ◽  
Texture Feature ◽  
Region Of Interest ◽  
Computation Time ◽  
Morphological Operations ◽  
Texture Feature Extraction ◽  
Mammogram Images

Recognition of either masses or tissues in a mammogram digital images is a key issue for radiologist. Present methods uses medial filter and morphological operations for detection of suspected cases in a mammogram. They use region of interest (ROI) segmentation for extraction of masses and classification of levels of severities.  Classification of large number of mammogram images based on breast cancer cases takes longer computation time for performing of ROI segmentation.  This is addressed by multi-ROI segmentation and it retrieves the textual properties of large mammogram images for effectively determining the breast cancer mammogram images.Experimental results shows the better performance of proposed method than existing ROI based texture feature extraction.

scholarly journals MRI Segmentation of the Human Brain: Challenges, Methods, and Applications

2015 ◽  
Vol 2015 ◽  
pp. 1-23 ◽  
Author(s):  
Ivana Despotović ◽  
Bart Goossens ◽  
Wilfried Philips
Keyword(s):  
Image Segmentation ◽  
Medical Image Analysis ◽  
Brain Mri ◽  
Bias Field ◽  
Mri Segmentation ◽  
Anatomical Structures ◽  
Segmentation Methods ◽  
Brain Changes ◽  
The Brain ◽  
Segmentation Problem

Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain’s anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and image-guided interventions. In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In this paper we review the most popular methods commonly used for brain MRI segmentation. We highlight differences between them and discuss their capabilities, advantages, and limitations. To address the complexity and challenges of the brain MRI segmentation problem, we first introduce the basic concepts of image segmentation. Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue. Finally, after reviewing different brain MRI segmentation methods, we discuss the validation problem in brain MRI segmentation.

scholarly journals Toward Effective Medical Image Analysis Using Hybrid Approaches—Review, Challenges and Applications

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 155
Author(s):  
Sami Bourouis ◽  
Roobaea Alroobaea ◽  
Saeed Rubaiee ◽  
Anas Ahmed
Keyword(s):  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Region Of Interest ◽  
Vital Role ◽  
Tumor Segmentation ◽  
Complex Data ◽  
Brain Tumor Segmentation ◽  
Hybrid Approaches ◽  
Data Volume

Accurate medical images analysis plays a vital role for several clinical applications. Nevertheless, the immense and complex data volume to be processed make difficult the design of effective algorithms. The first aim of this paper is to examine this area of research and to provide some relevant reference sources related to the context of medical image analysis. Then, an effective hybrid solution to further improve the expected results is proposed here. It allows to consider the benefits of the cooperation of different complementary approaches such as statistical-based, variational-based and atlas-based techniques and to reduce their drawbacks. In particular, a pipeline framework that involves different steps such as a preprocessing step, a classification step and a refinement step with variational-based method is developed to identify accurately pathological regions in biomedical images. The preprocessing step has the role to remove noise and improve the quality of the images. Then the classification is based on both symmetry axis detection step and non linear learning with SVM algorithm. Finally, a level set-based model is performed to refine the boundary detection of the region of interest. In this work we will show that an accurate initialization step could enhance final performances. Some obtained results are exposed which are related to the challenging application of brain tumor segmentation.

scholarly journals Segmentation of Thermogram Based on Region Based Technique using Split and Merge Method

2019 ◽  
Vol 8 (4) ◽  
pp. 9574-9578
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Object Representation ◽  
Region Of Interest ◽  
Region Growing ◽  
Destructive Testing ◽  
Segmentation Methods ◽  
Split And Merge ◽  
Thermographic Image ◽  
Non Destructive

The main aim of segmentation is to identify the Region of Interest for image analysis. The segregation of an image into meaningful structures is often an important phase in image analysis, object representation, visualization and also in various other image processing tasks. Image segmentation is mostly useful in applications like detection where it is difficult to process whole image at a time. In this paper Region based image segmentation is used to identify the delaminations in Thermographic image of Infrared Non-Destructive Testing. There are two basic techniques in Region based segmentation viz. Region growing method, splitting and merging method. New method based Split and Merge segmentation technique is employed to identify the defective regions in thermogram. Results obtained after segmentation as compared with state of art segmentation methods

scholarly journals Acceleration of Image Segmentation Algorithm for (Breast) Mammogram Images Using High-Performance Reconfigurable Dataflow Computers

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Ivan L. Milankovic ◽  
Nikola V. Mijailovic ◽  
Nenad D. Filipovic ◽  
Aleksandar S. Peulic
Keyword(s):  
Breast Cancer ◽  
Image Segmentation ◽  
Cancer Detection ◽  
High Performance ◽  
Region Of Interest ◽  
Breast Cancer Detection ◽  
Large Sets ◽  
Breast Image ◽  
Mammogram Images ◽  
Image Segmentation Algorithm

Image segmentation is one of the most common procedures in medical imaging applications. It is also a very important task in breast cancer detection. Breast cancer detection procedure based on mammography can be divided into several stages. The first stage is the extraction of the region of interest from a breast image, followed by the identification of suspicious mass regions, their classification, and comparison with the existing image database. It is often the case that already existing image databases have large sets of data whose processing requires a lot of time, and thus the acceleration of each of the processing stages in breast cancer detection is a very important issue. In this paper, the implementation of the already existing algorithm for region-of-interest based image segmentation for mammogram images on High-Performance Reconfigurable Dataflow Computers (HPRDCs) is proposed. As a dataflow engine (DFE) of such HPRDC, Maxeler’s acceleration card is used. The experiments for examining the acceleration of that algorithm on the Reconfigurable Dataflow Computers (RDCs) are performed with two types of mammogram images with different resolutions. There were, also, several DFE configurations and each of them gave a different acceleration value of algorithm execution. Those acceleration values are presented and experimental results showed good acceleration.

scholarly journals Anisotropic Fast Marching in ITK

2015 ◽  
Author(s):  
Jean-marie Mirebeau
Keyword(s):  
Image Segmentation ◽  
Motion Planning ◽  
Path Length ◽  
Computation Time ◽  
Front Propagation ◽  
Fast Marching ◽  
Segmentation Methods ◽  
Fast Marching Algorithm ◽  
Path Direction ◽  
Unit Of Length

The Fast Marching algorithm is an efficient numerical method for computing the distance and shortest path between points of a domain. For that purpose, it solves a front propagation problem, which can be of interest in itself. The method has numerous applications, ranging from motion planning to image segmentation. The unit of length, for computing the path length, may vary on the domain. Motivated by applications, we generalize the algorithm to the case where the unit of length also depends on the path direction. Segmentation methods can take advantage of this flexibility to achieve greater sensitivity and specificity, for a comparable computation time.

scholarly journals A Study on Image Segmentation Method for Image Processing

2021 ◽  
Author(s):  
S. Prabu ◽  
J.M. Gnanasekar
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Surveillance System ◽  
Vital Role ◽  
Important Process ◽  
Image Processing Techniques ◽  
Segmentation Methods ◽  
And Performance ◽  
Segmentation Image ◽  
Processing Techniques

Image processing techniques are essential part of the current computer technologies and that it plays vital role in various applications like medical field, object detection, video surveillance system, computer vision etc. The important process of Image processing is Image Segmentation. Image Segmentation is the process of splitting the images into various tiny parts called segments. Image processing makes to simplify the image representation in order to analyze the images. So many algorithms are developed for segmenting images, based on the certain feature of the pixel. In this paper different algorithms of segmentation can be reviewed, analyzed and finally list out the comparison for all the algorithms. This comparison study is useful for increasing accuracy and performance of segmentation methods in various image processing domains.

scholarly journals An Overview of Segmentation Algorithms for the Analysis of Anomalies on Medical Images

2018 ◽  
Vol 29 (1) ◽  
pp. 612-625 ◽  
Author(s):  
Subbiahpillai Neelakantapillai Kumar ◽  
Alfred Lenin Fred ◽  
Paul Sebastin Varghese
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Images ◽  
Computation Time ◽  
Vital Role ◽  
Medical Image Segmentation ◽  
Body Parts ◽  
Medical Practitioners ◽  
Segmentation Algorithms ◽  
The Right

Abstract Human disease identification from the scanned body parts helps medical practitioners make the right decision in lesser time. Image segmentation plays a vital role in automated diagnosis for the delineation of anatomical organs and anomalies. There are many variants of segmentation algorithms used by current researchers, whereas there is no universal algorithm for all medical images. This paper classifies some of the widely used medical image segmentation algorithms based on their evolution, and the features of each generation are also discussed. The comparative analysis of segmentation algorithms is done based on characteristics like spatial consideration, region continuity, computation complexity, selection of parameters, noise immunity, accuracy, and computation time. Finally, in this work, some of the typical segmentation algorithms are implemented on real-time datasets using Matlab 2010 software, and the outcome of this work will be an aid for the researchers in medical image processing.

Segmentation In Medical Resonance images to extract the cancerous nodule for early diagnosis on cancer

2012 ◽  
Vol 3 (2) ◽  
pp. 253-255
Author(s):  
Raman Brar
Keyword(s):  
Human Lung ◽  
Research Work ◽  
Vital Role ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Background Segmentation ◽  
Lung Mri

Image segmentation plays a vital role in several medical imaging programs by assisting the delineation of physiological structures along with other parts. The objective of this research work is to segmentize human lung MRI (Medical resonance Imaging) images for early detection of cancer.Watershed Transform Technique is implemented as the Segmentation method in this work. Some comparative experiments using both directly applied watershed algorithm and after marking foreground and computed background segmentation methods show the improved lung segmentation accuracy in some image cases.

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