Automatic renal lesion segmentation in ultrasound images based on saliency features, improved LBP, and an edge indicator under level set framework

2017 ◽  
Vol 45 (1) ◽  
pp. 223-235 ◽  
Author(s):  
Luying Gui ◽  
Xiaoping Yang
Keyword(s):  
Level Set ◽  
Renal Lesion ◽  
Ultrasound Images ◽  
Lesion Segmentation ◽  
Edge Indicator ◽  
Level Set Framework

scholarly journals Biometric Foetal Contour Extraction using Hybrid Level Set

2020 ◽  
Author(s):  
Rachana Jaiswal ◽  
Srikant Satarkar
Keyword(s):  
Feature Extraction ◽  
Level Set ◽  
Medical Images ◽  
Traditional Approach ◽  
Ultrasound Images ◽  
Contour Extraction ◽  
Processing Technologies ◽  
Biometric Measurements ◽  
Ultrasonic Images ◽  
Automatic Technique

In medical imaging, accurate anatomical structure extraction is important for diagnosis and therapeutic interventional planning. So, for easier, quicker and accurate diagnosis of medical images, image processing technologies may be employed in analysis and feature extraction of medical images. In this paper, some modifications to level set algorithm are made and modified algorithm is used for extracting contour of foetal objects in an image. The proposed approach is applied on foetal ultrasound images. In traditional approach, foetal parameters are extracted manually from ultrasound images. Due to lack of consistency and accuracy of manual measurements, an automatic technique is highly desirable to obtain foetal biometric measurements. This proposed approach is based on global & local region information for foetal contour extraction from ultrasonic images. The primary goal of this research is to provide a new methodology to aid the analysis and feature extraction from foetal images.

scholarly journals Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss

2021 ◽  
pp. 515-525
Author(s):  
Youbao Tang ◽  
Jinzheng Cai ◽  
Ke Yan ◽  
Lingyun Huang ◽  
Guotong Xie ◽  
...  
Keyword(s):  
Level Set ◽  
Regional Level ◽  
Lesion Segmentation ◽  
Weakly Supervised

A multiphase level set method for stroke lesion segmentation

2020 ◽  
Author(s):  
Ming Yang ◽  
Yun He ◽  
Xue Shi ◽  
Zhongping Chen ◽  
Dan Tong ◽  
...  
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Lesion Segmentation ◽  
Stroke Lesion

Modified Distance Regularized Level Set Segmentation Based Analysis for Kidney Stone Detection

2015 ◽  
Vol 2 (2) ◽  
pp. 24-41 ◽  
Author(s):  
K. Viswanath ◽  
R. Gunasundari
Keyword(s):  
Energy Level ◽  
Level Set ◽  
High Speed ◽  
Gabor Filter ◽  
Energy Levels ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Low Contrast ◽  
Level Set Function ◽  
Level Set Segmentation

The abnormalities of the kidney can be identified by ultrasound imaging. The kidney may have structural abnormalities like kidney swelling, change in its position and appearance. Kidney abnormality may also arise due to the formation of stones, cysts, cancerous cells, congenital anomalies, blockage of urine etc. For surgical operations it is very important to identify the exact and accurate location of stone in the kidney. The ultrasound images are of low contrast and contain speckle noise. This makes the detection of kidney abnormalities rather challenging task. Thus preprocessing of ultrasound images is carried out to remove speckle noise. In preprocessing, first image restoration is done to reduce speckle noise then it is applied to Gabor filter for smoothening. Next the resultant image is enhanced using histogram equalization. The preprocessed ultrasound image is segmented using distance regularized level set segmentation (DR-LSS), since it yields better results. It uses a two-step splitting methods to iteratively solve the DR-LSS equation, first step is iterating LSS equation, and then solving the Sign distance equation. The second step is to regularize the level set function which is the obtained from first step for better stability. The DR is included for LSS for eliminating of anti-leakages on image boundary. The DR-LSS does not require any expensive re-initialization and it is very high speed of operation. The RD-LSS results are compared with distance regularized level set evolution DRLSE1, DRLSE2 and DRLSE3. Extracted region of the kidney after segmentation is applied to Symlets (Sym12), Biorthogonal (bio3.7, bio3.9 & bio4.4) and Daubechies (Db12) lifting scheme wavelet subbands to extract energy levels. These energy level gives an indication about presence of stone in that particular location which significantly vary from that of normal energy level. These energy levels are trained by Multilayer Perceptron (MLP) and Back Propagation (BP) ANN to identify the type of stone with an accuracy of 98.6%.

Level set evolution with intensity prior knowledge for multiple sclerosis lesion segmentation

2019 ◽  
Vol 5 (1) ◽  
pp. 37
Author(s):  
Guodong Zhang ◽  
Zhaoxuan Gong ◽  
Wei Guo ◽  
Zhenyu Zhu ◽  
Jia Guo ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Prior Knowledge ◽  
Level Set ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation ◽  
Level Set Evolution
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