3-Dimensional Brain MRI Segmentation Based on Multi-Layer Background Subtraction and Seed Region Growing Algorithm

Segmentation of brain MRI in an image sequence is one of the most challenging problems in image processing, while at the same time one that finds numerous applications. In this paper, we propose a robust multi-layer background subtraction technique and seed region growing approach which takes advantages of local texture features represented by local binary patterns (LBP) and photometric invariant color measurements in RGB color space. Due to the use of hybridization of layer-based strategy and seed region growing approach, the approach can model moving background pixels with quasiperiodic flickering as well as background scenes which may vary over time due to the addition and removal of long-time stationary objects. The experiment results prove that in the view of the brain MRI segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.

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A Novel Approach Based on Multi-Layer Background Subtraction and Seed Region Growing for Sport Graphics Segmentation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.3358 ◽

2014 ◽

Vol 513-517 ◽

pp. 3358-3361

Author(s):

Li Liang ◽

Hong Wei Wang

Keyword(s):

Background Subtraction ◽

Color Space ◽

Region Growing ◽

Texture Features ◽

Local Binary Patterns ◽

Seed Region ◽

Novel Approach ◽

Segmentation Quality ◽

Stationary Objects ◽

Long Time

Segmentation of motion in an image sequence is one of the most challenging problems in image processing, while at the same time one that finds numerous applications. In this paper, we propose a robust multi-layer background subtraction technique and seed region growing approach which takes advantages of local texture features represented by local binary patterns (LBP) and photometric invariant color measurements in RGB color space. Due to the use of hybridization of layer-based strategy and seed region growing approach, the approach can model moving background pixels with quasiperiodic flickering as well as background scenes which may vary over time due to the addition and removal of long-time stationary objects. The experiment results prove that in the view of the sport image segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.

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Multiple Motion Segmentation of Sport Image Based on Multi-Layer Background Subtraction

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.846-847.1266 ◽

2013 ◽

Vol 846-847 ◽

pp. 1266-1269

Author(s):

Meng Zhao ◽

Zhan Ping Li

Keyword(s):

Background Subtraction ◽

Color Space ◽

Texture Features ◽

Local Binary Patterns ◽

Growth Conditions ◽

Subtraction Method ◽

Subtraction Technique ◽

Stationary Objects ◽

Long Time ◽

Rgb Color Space

Segmentation of motion in an image sequence is one of the most challenging problems in image processing. In image analysis engineering, accurate statistics of sport image is one of the most important subjects, while at the same time one that finds numerous applications. In this paper, we propose a robust multi-layer background subtraction technique which takes advantages of local texture features represented by local binary patterns (LBP) and photometric invariant color measurements in RGB color space. Due to the use of a simple layer-based strategy, the approach can model moving background pixels with quasiperiodic flickering as well as background scenes which may vary over time due to the addition and removal of long-time stationary objects, which plays an important role in optimizing the growth conditions of sport image. Segmentation of sport images is successfully realized by means of multi-layer background subtraction method and then the sport image is computed precisely.

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Image Segmentation Algorithm Research for Sport Graphics Based on Multi-Layer Background Subtraction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.339.265 ◽

2013 ◽

Vol 339 ◽

pp. 265-268

Author(s):

Ming Jing Lu ◽

Zhan Ping Li

Keyword(s):

Background Subtraction ◽

Color Space ◽

Texture Features ◽

Bilateral Filter ◽

Local Binary Patterns ◽

Stationary Objects ◽

Long Time ◽

Rgb Color Space ◽

Image Segmentation Algorithm ◽

Similar Intensity

Segmentation of motion in an image sequence is one of the most challenging problems in image processing, while at the same time one that finds numerous applications. In this paper, we propose a robust multi-layer background subtraction technique which takes advantages of local texture features represented by local binary patterns (LBP) and photometric invariant color measurements in RGB color space. Due to the use of a simple layer-based strategy, the approach can model moving background pixels with quasiperiodic flickering as well as background scenes which may vary over time due to the addition and removal of long-time stationary objects. The use of a cross-bilateral filter allows to implicitly smooth detection results over regions of similar intensity and preserve object boundaries.

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Automatic Seeded Region Growing (ASRG) Using Genetic Algorithm for Brain MRI Segmentation

Wireless Personal Communications ◽

10.1007/s11277-019-06596-4 ◽

2019 ◽

Vol 109 (2) ◽

pp. 897-908 ◽

Cited By ~ 2

Author(s):

Ramin Dehdasht-Heydari ◽

Sadegh Gholami

Keyword(s):

Genetic Algorithm ◽

Brain Mri ◽

Region Growing ◽

Mri Segmentation ◽

Seeded Region Growing

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Classification of mammograms based on features extraction techniques using support vector machine

Computer Science and Information Technologies ◽

10.11591/csit.v2i3.p121-131 ◽

2020 ◽

Vol 2 (3) ◽

pp. 121-131

Author(s):

Enas Mohammed Hussein Saeed ◽

Hayder Adnan Saleh ◽

Enam Azez Khalel

Keyword(s):

Region Growing ◽

Texture Features ◽

Local Binary Patterns ◽

Features Extraction ◽

Support Vector ◽

Global Threshold ◽

Medical City ◽

Analysis Society ◽

Occurrence Matrix

Now mammography can be defined as the most reliable method for early breast cancer detection. The main goal of this study is to design a classifier model to help radiologists to provide a second view to diagnose mammograms. In the proposed system medium filter and binary image with a global threshold have been applied for removing the noise and small artifacts in the pre-processing stage. Secondly, in the segmentation phase, a Hybrid Bounding Box and Region Growing (HBBRG) algorithm are utilizing to remove pectoral muscles, and then a geometric method has been applied to cut the largest possible square that can be obtained from a mammogram which represents the ROI. In the features extraction phase three method was used to prepare texture features to be a suitable introduction to the classification process are first Order (statistical features), Local Binary Patterns (LBP), and Gray-Level Co-Occurrence Matrix (GLCM), Finally, SVM has been applied in two-level to classify mammogram images in the first level to normal or abnormal, and then the classification of abnormal once in the second level to the benign or malignant image. The system was tested on the MAIS the Mammogram image analysis Society (MIAS) database, in addition to the image from the Teaching Oncology Hospital, Medical City in Baghdad, where the results showed achieving an accuracy of 95.454% for the first level and 97.260% for the second level, also, the results of applying the proposed system to the MIAS database alone were achieving an accuracy of 93.105% for the first level and 94.59 for the second level.

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Image Retrieval with Fusion of Thepade’s Sorted Block Truncation Coding n-ary based Color and Local Binary Pattern based Texture Features with Different Color Places

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.e1963.039520 ◽

2020 ◽

Vol 9 (5) ◽

pp. 28-34

Keyword(s):

Image Retrieval ◽

Local Binary Pattern ◽

Feature Vector ◽

Color Space ◽

Texture Features ◽

Local Binary Patterns ◽

Block Truncation Coding ◽

Local Texture ◽

Color Features ◽

Different Color

In these years, there has been a gigantic growth in the generation of data. Innovations such as the Internet, social media and smart phones are the facilitators of this information boom. Since ancient times images were treated as an effective mode of communication. Even today most of the data generated is image data. The technology for capturing, storing and transferring images is well developed but efficient image retrieval is still a primitive area of research. Content Based Image Retrieval (CBIR) is one such area where lot of research is still going on. CBIR systems rely on three aspects of the image content namely texture, shape and color. Application specific CBIR systems are effective whereas Generic CBIR systems are being explored. Previously, descriptors are used to extract shape, color or texture content features, but the effect of using more than one descriptor is under research and may yield better results. The paper presents the fusion of TSBTC n-ary (Thepade's Sorted n-ary Block Truncation Coding) Global Color Features and Local Binary Pattern (LBP) Local Texture Features in Content Based Image with Different Color Places TSBTC n-ary devises global color features from an image. It is a faster and better technique compared to Block Truncation Coding. It is also rotation and scale invariant. When applied on an image TSBTC n-ary gives a feature vector based on the color space, if TSBTC n-ary is applied on the obtained LBP (Local Binary Patterns) of the image color planes, the feature vector obtained is be based on local texture content. Along with RGB, the Luminance chromaticity color space like YCbCr and Kekre’s LUV are also used in experimentation of proposed CBIR techniques. Wang dataset has been used for exploration of proposed method. It consists of 1000 images (10 categories having 100 images each). Obtained results have shown performance improvement using fusion of BTC extracted global color features and local texture features extracted with TSBTC n-ary applied on Local Binary Patterns (LBP).

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Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405616666210104111218 ◽

2021 ◽

Vol 16 ◽

Author(s):

Ghazanfar Latif ◽

Jaafar Alghazo ◽

Fadi N. Sibai ◽

D.N.F. Awang Iskandar ◽

Adil H. Khan

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Brain Mri ◽

Tumor Segmentation ◽

Brain Anatomy ◽

Mri Segmentation ◽

Brain Tumor Segmentation ◽

Brain Images ◽

Low Contrast ◽

Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

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