Accurate Liver Border Identification Model in CT Scan Images

2021 ◽  
pp. 2150039
Author(s):  
Sangeeta K. Siri ◽  
S. Pramod Kumar ◽  
Mrityunjaya V. Latte
Keyword(s):  
Ct Scan ◽  
Intensity Distribution ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Pixel Intensity ◽  
Chain Code ◽  
Computer Based ◽  
Scanned Images ◽  
Liver Region ◽  
Liver Image

The liver is an important organ in human body with certain variations in its edges, color, shape and pixel intensity distribution. These uncertainties may be because of various liver pathologies, hereditary or both. Along with it, liver has close proximity to its nearby organs. Hence, identifying liver in scanned images is a challenging step in image processing. This task becomes more imprecise when liver diseases are present at the edges. The liver segmentation is prerequisite for liver volumetry, computer-based surgery planning, liver surgery modelling, surgery training, 3D view generation, etc. The proposed hybrid segmentation method overcomes the problems and identifies liver boundary in Computed-Tomography (CT) scan images accurately. In this paper, the first step is to study statistics of pixel intensity distribution within liver image, and novel methodology is designed to obtain thresholds. Then, threshold-based segmentation is applied which separates the liver from abdominal CT scan images. In the second step, liver edge is corrected using improved chain code and Bresenham pixel interconnection methods. This provides a precise liver image. The initial points are located inside the liver region without user interventions. These initial points evolve outwardly using Fast Marching Method (FMM), identifying the liver boundary accurately in CT abdominal scan images.

scholarly journals A Novel Approach to Extract Exact Liver Image Boundary from Abdominal CT Scan using Neutrosophic Set and Fast Marching Method

2019 ◽  
Vol 28 (4) ◽  
pp. 517-532 ◽  
Author(s):  
Sangeeta K. Siri ◽  
Mrityunjaya V. Latte
Keyword(s):  
Ct Scan ◽  
Median Filter ◽  
Neutrosophic Set ◽  
Intensity Level ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Abdominal Ct ◽  
Novel Approach ◽  
Abdominal Ct Scan ◽  
Marching Method

Abstract Liver segmentation from abdominal computed tomography (CT) scan images is a complicated and challenging task. Due to the haziness in the liver pixel range, the neighboring organs of the liver have the same intensity level and existence of noise. Segmentation is necessary in the detection, identification, analysis, and measurement of objects in CT scan images. A novel approach is proposed to meet the challenges in extracting liver images from abdominal CT scan images. The proposed approach consists of three phases: (1) preprocessing, (2) CT scan image transformation to neutrosophic set, and (3) postprocessing. In preprocessing, noise in the CT scan is reduced by median filter. A “new structure” is introduced to transform a CT scan image into a neutrosophic domain, which is expressed using three membership subsets: true subset (T), false subset (F), and indeterminacy subset (I). This transform approximately extracts the liver structure. In the postprocessing phase, morphological operation is performed on the indeterminacy subset (I). A novel algorithm is designed to identify the start points within the liver section automatically. The fast marching method is applied at start points that grow outwardly to detect the accurate liver boundary. The evaluation of the proposed segmentation algorithm is concluded using area- and distance-based metrics.

Universal Liver Extraction Algorithm: An Improved Chan–Vese Model

2018 ◽  
Vol 29 (1) ◽  
pp. 237-250 ◽  
Author(s):  
Sangeeta K. Siri ◽  
Mrityunjaya V. Latte
Keyword(s):  
Ct Scan ◽  
Median Filter ◽  
Disease Diagnosis ◽  
Initial Contour ◽  
Mri Scan ◽  
Pixel Intensity ◽  
Abdominal Mri ◽  
Speed Up ◽  
Magnetic Resonance Imaging Mri ◽  
Liver Image

Abstract Liver segmentation is important to speed up liver disease diagnosis. It is also useful for detection, recognition, and measurement of objects in liver images. Sufficient work has been carried out until now, but common methodology for segmenting liver image from CT scan, MRI scan, PET scan, etc., is not available. The proposed methodology is an effort toward developing a general algorithm to segment liver image from abdominal computerized tomography (CT) scan and magnetic resonance imaging (MRI) scan images. In the proposed algorithm, pixel intensity range of the liver portion is obtained by cropping a random section of the liver. Using its histogram, threshold values are calculated. Further, threshold-based segmentation is performed, which separates liver from abdominal CT scan image/abdominal MRI scan image. Noise in the liver image is reduced using median filter, and the quality of the image is improved by sigmoidal function. The image is then converted into binary image. The Chan–Vese (C–V) model demands an initial contour, which evolves outward. A novel algorithm is proposed to identify the initial contour inside the liver without user intervention. This initial contour propagates outward and continues until the boundary of the liver is identified accurately. This process terminates by itself when the entire boundary of the liver is detected. The method has been validated on CT images and MRI images. Results on the variety of images are compared with existing algorithms, which reveal its robustness, effectiveness, and efficiency.

scholarly journals Universal Fast Marching Method to Identify Liver Image

2020 ◽  
Vol 925 ◽  
pp. 012032
Author(s):  
Sangeeta K Siri ◽  
S Pramod Kumar ◽  
Gavisiddappa
Keyword(s):  
Fast Marching Method ◽  
Fast Marching ◽  
Marching Method ◽  
Liver Image

scholarly journals A Comparison of Local Path Planning Techniques of Autonomous Surface Vehicles for Monitoring Applications: The Ypacarai Lake Case-study

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1488
Author(s):  
Federico Peralta ◽  
Mario Arzamendia ◽  
Derlis Gregor ◽  
Daniel G. Reina ◽  
Sergio Toral
Keyword(s):  
Path Planning ◽  
Random Trees ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Planning Techniques ◽  
Advantages And Disadvantages ◽  
Autonomous Surface Vehicle ◽  
Local Path Planning ◽  
Custom Made ◽  
Local Path

Local path planning is important in the development of autonomous vehicles since it allows a vehicle to adapt their movements to dynamic environments, for instance, when obstacles are detected. This work presents an evaluation of the performance of different local path planning techniques for an Autonomous Surface Vehicle, using a custom-made simulator based on the open-source Robotarium framework. The conducted simulations allow to verify, compare and visualize the solutions of the different techniques. The selected techniques for evaluation include A*, Potential Fields (PF), Rapidly-Exploring Random Trees* (RRT*) and variations of the Fast Marching Method (FMM), along with a proposed new method called Updating the Fast Marching Square method (uFMS). The evaluation proposed in this work includes ways to summarize time and safety measures for local path planning techniques. The results in a Lake environment present the advantages and disadvantages of using each technique. The proposed uFMS and A* have been shown to achieve interesting performance in terms of processing time, distance travelled and security levels. Furthermore, the proposed uFMS algorithm is capable of generating smoother routes.

A Generalized Fast Marching Method on Unstructured Triangular Meshes

2013 ◽  
Vol 51 (6) ◽  
pp. 2999-3035 ◽  
Author(s):  
E. Carlini ◽  
M. Falcone ◽  
Ph. Hoch
Keyword(s):  
Fast Marching Method ◽  
Triangular Meshes ◽  
Fast Marching ◽  
Marching Method

scholarly journals Tsunami wave propagation by voronoi diagram

2018 ◽  
Vol 7 (3) ◽  
pp. 1233
Author(s):  
V Yuvaraj ◽  
S Rajasekaran ◽  
D Nagarajan
Keyword(s):  
Wave Propagation ◽  
Voronoi Diagram ◽  
Tsunami Wave ◽  
Fast Marching Method ◽  
Coastal Regions ◽  
Fast Marching ◽  
Tsunami Waves ◽  
The Finite Difference Method ◽  
Run Up ◽  
Marching Method

Cellular automata is the model applied in very complicated situations and complex problems. It involves the Introduction of voronoi diagram in tsunami wave propagation with the help of a fast-marching method to find the spread of the tsunami waves in the coastal regions. In this study we have modelled and predicted the tsunami wave propagation using the finite difference method. This analytical method gives the horizontal and vertical layers of the wave run up and enables the calculation of reaching time.  

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