scholarly journals Automatic segmentation of coronary morphology using transmittance-based lumen intensity-enhanced intravascular optical coherence tomography images and applying a localized level-set-based active contour method

2016 ◽  
Vol 3 (04) ◽  
pp. 1 ◽  
Author(s):  
Shiju Joseph ◽  
Asif Adnan ◽  
David Adlam
Keyword(s):  
Optical Coherence Tomography ◽  
Level Set ◽  
Active Contour ◽  
Automatic Segmentation ◽  
Contour Method ◽  
Optical Coherence ◽  
Active Contour Method ◽  
Intravascular Optical Coherence Tomography

scholarly journals Automatic Liver Cancer Segmentation using active contour model and RF Classifier

2019 ◽  
Vol 8 (11) ◽  
pp. 1139-1142
Keyword(s):  
Random Forest ◽  
Liver Cancer ◽  
Active Contour ◽  
Active Contour Model ◽  
Automatic Segmentation ◽  
Computation Time ◽  
New Method ◽  
Contour Method ◽  
Active Contour Method ◽  
Cancer Tumor

In this paper, we presented the new method for liver cancer detection. Computed Tomography (CT) has becomes important tool for diagnosis of liver cancer. The proposed method used in this paper is Random Forest (RF) classifier algorithm for the detection of cancer in the liver. For the automatic segmentation, here we use active contour method to segment the liver and liver cancer to rectify the manual segmentation problem. It is fully automatic and the proposed classifier will successfully classifies whether it is malignant or benign liver cancer tumor. Manual identification is not accurate and also time consuming task. The new method proposed in this paper will segment the liver cancer from the CT image of liver automatically. It is highly accurate and less computation time. The experiment results show the accuracy of the proposed method. Random Forest classifier has 91% accuracy rate and less error rate and achieved excellent test result

scholarly journals Automatic segmentation of Colon Cancer Cells Based on Active Contour Method: A New Approach

2013 ◽  
Vol 7 (2) ◽  
Author(s):  
Jamal Charara ◽  
Alaa Hilal ◽  
Ali Al Houseini ◽  
Walid Hassan ◽  
Mohamad Nassreddine
Keyword(s):  
Colon Cancer ◽  
Cancer Cells ◽  
Active Contour ◽  
Automatic Segmentation ◽  
Contour Method ◽  
Colon Cancer Cells ◽  
New Approach ◽  
Active Contour Method

scholarly journals Automatic Segmentation and Classification of Liver Tumor using Hybrid Neural Network

2020 ◽  
Vol 9 (1) ◽  
pp. 1807-1811
Keyword(s):  
Liver Tumor ◽  
Active Contour ◽  
Automatic Segmentation ◽  
Back Propagation ◽  
Contour Method ◽  
Tumor Segmentation ◽  
Hybrid Neural Network ◽  
Active Contour Method ◽  
Segmentation Problem

Liver tumor is most common nowadays. Liver tumor segmentation is one of the most essential steps in treating it. We have chosen CT scan image for liver tumor diagnosis. Accurate tumor segmentation is done using computed tomography (CT) images. Since the manual identification is not that much accurate and time consuming, we go for active contour method. This automatic segmentation method is highly accurate and provides very less time for computation. The back propagation classifier method has a very good accuracy rate and a very less error rate and hence achieved the best result. The proposed method we used in this paper is back propagation classification algorithm for the detection of early and final stages of liver tumor. For the automatic segmentation, we use an active contour method to segment the liver and liver tumor to overcome the manual segmentation problem. This is an automatic method will help us to know whether the tumor is in benign or malignant stage.

Semi-Automatic Segmentation of Ct/Mri Images Based on Active Contour Method for 3D Reconstruction of Abdominal Aortic Aneurysms

2014 ◽  
Vol 19 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Paweł Drapikowski ◽  
Zuzanna Domagała
Keyword(s):  
Active Contour ◽  
Automatic Segmentation ◽  
Aortic Aneurysms ◽  
Contour Method ◽  
Rupture Risk ◽  
Segmentation Method ◽  
Segmentation Process ◽  
Abdominal Aortic ◽  
Active Contour Method ◽  
Mri Image

Abstract Abstract The paper presents a CT/MRI image based semi-automatic AAA (abdominal aortic aneurysm) segmentation method. Segmentation process can run automatically with the active contour method but results are controlled by the operator. If incorrect segmentation is noticed, the operator may introduce corrections. The proposed method makes possible the segmentation of dissected aneurysms, with which no automatic analysis works. Controlling the segmentation process by the operator serves to ensure correct geometric shape reproduction, which is crucial in deploying aneurysm models to help assess rupture risk.

Feature Based Active Contour Method for Automatic Detection of Breast Lesions Using Ultrasound Images

2014 ◽  
Vol 573 ◽  
pp. 471-476 ◽  
Author(s):  
Telagarapu Prabhakar ◽  
S. Poonguzhali
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Active Contour ◽  
Automatic Segmentation ◽  
Ultrasound Image ◽  
Contour Method ◽  
Speckle Reduction ◽  
Initial Contour ◽  
Ultrasound Images ◽  
Active Contour Method

Breast cancer has been increasing over the past three decades. Early detection of breast cancer is crucial for an effective treatment. Mammography is used for early detection and screening. Especially for young women, mammography procedures may not be very comfortable. Ultrasound has been one of the most powerful techniques for imaging organs and soft tissue structure in the human body. It has been used for breast cancer detection because of its non-invasive, sensitive to dense breast, low positive rate and cheap cost. But due to the nature of ultrasound image, the image suffers from poor quality caused by speckle noise. These make the automatic segmentation and classification of interested lesion difficult. One of the frequently used segmentation method is active contour. If this initial contour of active contour method is selected outside the region of interest, final segmentation and classification would be definitely incorrect. So, mostly the initial contour is manually selected in order to avoid incorrect segmentation and classification. Here implementing a method which was able to locate the initial contour automatically within the multiple lesion regions by using the wavelet soft threshold speckle reduction method, statistical features of the lesion regions and neural network and also we are able to automatically segment the lesion regions properly. This will help the radiologist to identify the lesion boundary automatically.

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