scholarly journals Semi-automatic Segmentation of Liver Tumors from CT Scans Using Bayesian Rule-based 3D Region Growing

2008 ◽  
Author(s):  
Yingyi Qi ◽  
Wei Xiong ◽  
Wee Keng Leow ◽  
Qi Tian ◽  
Jiayin Zhou ◽  
...  
Keyword(s):  
Liver Tumors ◽  
Automatic Segmentation ◽  
Three Dimensional ◽  
Region Growing ◽  
Ct Scans ◽  
Tumor Segmentation ◽  
Morphological Operations ◽  
Model Matching ◽  
Bayesian Decision ◽  
Seeded Region Growing

Automatic segmentation of liver tumorous regions often fails due to high noise and large variance of tumors. In this work, a semi-automatic algorithm is proposed to segment liver tumors from computed tomography (CT) images. To cope with the variance of tumors, their intensity probability density functions (PDF) are modeled as a bag of Gaussians unlike the previous works where the tumor is modeled as a single Gaussian, and employ a three-dimensional seeded region growing (SRG) method. The bag of Gaussians are initialized at manually selected seeds and updated during growing process iteratively. There are two criteria to be fulfilled for growing: one is the Bayesian decision rule, and the other is a model matching measure. Once the growing is terminated, morphological operations are performed to refine the result. This method, showing promising performance, has been evaluated using ten CT scans of livers with twenty tumors provided by the organizer of the 3D Liver Tumor Segmentation Challenge 2008.

scholarly journals Automated tumor segmentation in MR brain image using fuzzy c-means clustering and seeded region methodology

2021 ◽  
Vol 10 (2) ◽  
pp. 284
Author(s):  
Mustafa Zuhaer Nayef AL-Dabagh
Keyword(s):  
Region Growing ◽  
Automated System ◽  
Tumor Segmentation ◽  
Morphological Operations ◽  
Jaccard Coefficient ◽  
Fuzzy C Means ◽  
Seeded Region Growing ◽  
Processing Step ◽  
Fuzzy C Means Clustering ◽  
Comparison Of The Results

<span id="docs-internal-guid-c8cba487-7fff-2314-f38a-f2936a74e0fd"><span>Automated segmentation of a tumor is still a considerably exciting research topic in the medical imaging processing field, and it plays a considerable role in forming a right diagnosis, to aid effective medical treatment. In this work, a fully automated system for segmentation of the brain tumor in MRI images is introduced. The suggested system consists of three parts: Initially, the image is pre-processed to enhance contrast, eliminate noise, and strip the skull from the image using filtering and morphological operations. Secondly, segmentation of the image happens using two techniques, fuzzy c-means clustering (FCM) and with the application of a seeded region growing algorithm (SGR). Thirdly, this method proposes a post-processing step to smooth segmentation region edges using morphological operations. The testing of the proposed system involved 233 patients, which included 287 MRI images. A comparison of the results ensued, with the manual verification of the traces performed by doctors, which ultimately proved an average Dice Coefficient of 90.13% and an average Jaccard Coefficient of 82.60% also, by comparison with traditional segmentation techniques such as FCM method. The segmentation results and quantitative data analysis demonstrates the effectiveness of the suggested system.</span></span>

scholarly journals Automatic Detection of Pulmonary Nodules using Three-dimensional Chain Coding and Optimized Random Forest

2020 ◽  
Vol 10 (7) ◽  
pp. 2346 ◽  
Author(s):  
May Phu Paing ◽  
Kazuhiko Hamamoto ◽  
Supan Tungjitkusolmun ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Random Forest ◽  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Region Growing ◽  
Favorable Result ◽  
Dimensional Chain ◽  
Chain Code ◽  
Seeded Region Growing ◽  
Chain Coding ◽  
Diagnosis Of Lung Cancer

The detection of pulmonary nodules on computed tomography scans provides a clue for the early diagnosis of lung cancer. Manual detection mandates a heavy radiological workload as it identifies nodules slice-by-slice. This paper presents a fully automated nodule detection with three significant contributions. First, an automated seeded region growing is designed to segment the lung regions from the tomography scans. Second, a three-dimensional chain code algorithm is implemented to refine the border of the segmented lungs. Lastly, nodules inside the lungs are detected using an optimized random forest classifier. The experiments for our proposed detection are conducted using 888 scans from a public dataset, and achieves a favorable result of 93.11% accuracy, 94.86% sensitivity, and 91.37% specificity, with only 0.0863 false positives per exam.

scholarly journals Validation of Liver Tumor Segmentation in CT Scans by Relating Manual and Algorithmic Performance - A Preliminary Study

2010 ◽  
Author(s):  
Jan hendrik Moltz ◽  
Jan Rühaak ◽  
Christiane Engel ◽  
Ulrike Kayser ◽  
Heinz-Otto Peitgen
Keyword(s):  
Liver Tumors ◽  
Ground Truth ◽  
Ct Scans ◽  
Tumor Segmentation ◽  
Validation Framework ◽  
Segmentation Quality ◽  
Segmentation Algorithms ◽  
Preliminary Study ◽  
Traditional Approaches

The development of segmentation algorithms for liver tumors in CT scans has found growing attention in recent years. The validation of these methods, however, is often treated as a subordinate task. In this article, we review existing approaches and present rst steps towards a new methodology that evaluates the quality of an algorithm in relation to the variability of manual delineations. We obtained three manual segmentations for 50 liver lesions and computed the results of a segmentation algorithm. We compared all four masks with each other and with different ground truth estimates and calculated scores according to the validation framework from the MICCAI challenge 2008. Our results show some cases where this more elaborate evaluation reflects the segmentation quality in a more adequate way than traditional approaches. The concepts can also be extended to other similar segmentation problems.

Certain Investigation Titles on the Segmentation of Colon and Removal of Opacified Fluid for Virtual Colonoscopy

2019 ◽  
pp. 114-138
Author(s):  
Gayathri Devi Krishnamoorthy ◽  
Kishore B.
Keyword(s):  
Virtual Colonoscopy ◽  
Automatic Segmentation ◽  
Region Growing ◽  
Computational Time ◽  
Manual Segmentation ◽  
Semiautomatic Segmentation ◽  
Seeded Region Growing ◽  
Multiple Datasets ◽  
The World ◽  
Clustering Approach

Colorectal cancer (CRC) is a most important type of cancer that can be detected by virtual colonoscopy (VC) in the colon or rectum, and it is the major cause of death prevailing in the world. The CAD technique requires the segmentation of the colon to be accurate and can be implemented by two approaches. The first approach focuses on the segmentation of lungs in the computed tomography (CT) images downloaded from The Cancer Imaging Archive (TCIA) using clustering approach. The second method focused on the automatic segmentation of colon, removal of opacified fluid and bowels for all the slices in a dataset in a sequential order using MATLAB. The second approach requires more computational time, and hence, in order to reduce, the semiautomatic segmentation of colon was implemented in 3D seeded region growing and fuzzy clustering approach in MEVISLAB software. The approaches were implemented in multiple datasets and the accuracy were verified with manual segmentation by radiologist, and the importance of removing opacified fluid were shown for improving the accuracy of colon segments.

Parallel Segmentation of Multi-Channel Images Using Multi-Dimentional Mathematical Morphology

2006 ◽  
pp. 270-291
Author(s):  
Antonio Plaza ◽  
Javier Plaza ◽  
David Valencia ◽  
Pablo Martiez
Keyword(s):  
Mathematical Morphology ◽  
Parallel Implementation ◽  
Region Growing ◽  
Large Data ◽  
Remotely Sensed ◽  
Morphological Operations ◽  
Remotely Sensed Data ◽  
Imaging Spectrometer ◽  
Seeded Region Growing ◽  
Multi Channel Imaging

Multi-channel images are characteristic of certain applications, such as medical imaging or remotely sensed data analysis. Mathematical morphology-based segmentation of multi-channel imagery has not been fully accomplished yet, mainly due to the lack of vector-based strategies to extend classic morphological operations to multidimensional imagery. For instance, the most important morphological approach for image segmentation is the watershed transformation, a hybrid of seeded region growing and edge detection. In this chapter, we describe a vector-preserving framework to extend morphological operations to multi-channel images, and further propose a fully automatic multi-channel watershed segmentation algorithm that naturally combines spatial and spectral/temporal information. Due to the large data volumes often associated with multi-channel imaging, this chapter also develops a parallel implementation strategy to speed up performance. The proposed parallel algorithm is evaluated using magnetic resonance images and remotely sensed hyperspectral scenes collected by the NASA Jet Propulsion Laboratory Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS).

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