Nuseiba Altarawneh - Academia.edu (original) (raw)

Papers by Nuseiba Altarawneh

Research paper thumbnail of Liver Segmentation from CT Images Using a Modified Distance Regularized Level Set Model Based on a Novel Balloon Force

Computer Science & Information Technology ( CS & IT ), 2014

ABSTRACT Organ segmentation from medical images is still an open problem and liver segmentation i... more ABSTRACT Organ segmentation from medical images is still an open problem and liver segmentation is a much more challenging task among other organ segmentations. This paper presents a liver segmentation method from a computer tomography images. We propose a novel balloon force that controls the direction of the evolution process and slows down the evolving contour in regions with weak or without edges and discourages the evolving contour from going far away from the liver boundary or from leaking at a region that has a weak edge, or does not have an edge. The model is implemented using a modified Distance Regularized Level Set (DRLS) model. The experimental results show that the method can achieve a satisfactory result. Comparing with the original DRLS model, our model is more effective in dealing with over segmentation problems.

Research paper thumbnail of Global Threshold and Region-Based Active Contour Model for Accurate Image Segmentation

Signal & Image Processing : An International Journal, 2014

ABSTRACT In this contribution, we develop a novel global threshold-based active contour model. Th... more ABSTRACT In this contribution, we develop a novel global threshold-based active contour model. This model deploys a new edge-stopping function to control the direction of the evolution and to stop the evolving contour at weak or blurred edges. An implementation of the model requires the use of selective binary and Gaussian filtering regularized level set (SBGFRLS) method. The method uses either a selective local or global segmentation property. It penalizes the level set function to force it to become a binary function. This procedure is followed by using a regularisation Gaussian. The Gaussian filters smooth the level set function and stabilises the evolution process. One of the merits of our proposed model stems from the ability to initialise the contour any where inside the image to extract object boundaries. The proposed method is found to perform well, notably when the intensities inside and outside the object are homogenous. Our method is applied with satisfactory results on various types of images, including synthetic, medical and Arabic-characters images.

Research paper thumbnail of A NOVEL GLOBAL THRESHOLD-BASED ACTIVE CONTOUR MODEL

In this paper, we propose a novel global threshold-based active contour model which employs a new... more In this paper, we propose a novel global threshold-based active contour model which employs a new edge-stopping function that controls the direction of the evolution and stops the evolving contour at weak or blurred edges. The model is implemented using selective binary and Gaussian filtering regularized level set (SBGFRLS) method. The method has a selective local or global segmentation property. It selectively penalizes the level set function to be a binary function. This is followed by using a Gaussian function to regularize it. The Gaussian filters smooth the level set function and afford the evolution more stability. The contour could be initialized anywhere inside the image to extract object boundaries. The proposed method performs well when the intensities inside and outside the object are homogenous. Our method is tested on synthetic, medical and Arabic characters images with satisfactory results

Research paper thumbnail of A MODIFIED DISTANCE REGULARIZED LEVEL SET MODEL FOR LIVER SEGMENTATION FROM CT IMAGES

Segmentation of organs from medical images is an active and interesting area of research. Liver s... more Segmentation of organs from medical images is an active and interesting area of research. Liver segmentation incurs more challenges and difficulties compared with segmentation of other organs. In this paper we demonstrate a liver segmentation method for computer tomography images. We revisit the distance regularization level set (DRLS) model by deploying new balloon forces. These forces control the direction of the evolution and slow down the evolution process in regions that are associated with weak or without edges. The newly added balloon forces discourage the evolving contour from exceeding the liver boundary or leaking at a region that is associated with a weak edge, or does not have an edge. Our experimental results confirm that the method yields a satisfactory overall segmentation outcome. Comparing with the original DRLS model, our model is proven to be more effective in handling oversegmentation problems.

Research paper thumbnail of Liver Segmentation from CT Images Using a Modified Distance Regularized Level Set Model Based on a Novel Balloon Force

Computer Science & Information Technology ( CS & IT ), 2014

ABSTRACT Organ segmentation from medical images is still an open problem and liver segmentation i... more ABSTRACT Organ segmentation from medical images is still an open problem and liver segmentation is a much more challenging task among other organ segmentations. This paper presents a liver segmentation method from a computer tomography images. We propose a novel balloon force that controls the direction of the evolution process and slows down the evolving contour in regions with weak or without edges and discourages the evolving contour from going far away from the liver boundary or from leaking at a region that has a weak edge, or does not have an edge. The model is implemented using a modified Distance Regularized Level Set (DRLS) model. The experimental results show that the method can achieve a satisfactory result. Comparing with the original DRLS model, our model is more effective in dealing with over segmentation problems.

Research paper thumbnail of Global Threshold and Region-Based Active Contour Model for Accurate Image Segmentation

Signal & Image Processing : An International Journal, 2014

ABSTRACT In this contribution, we develop a novel global threshold-based active contour model. Th... more ABSTRACT In this contribution, we develop a novel global threshold-based active contour model. This model deploys a new edge-stopping function to control the direction of the evolution and to stop the evolving contour at weak or blurred edges. An implementation of the model requires the use of selective binary and Gaussian filtering regularized level set (SBGFRLS) method. The method uses either a selective local or global segmentation property. It penalizes the level set function to force it to become a binary function. This procedure is followed by using a regularisation Gaussian. The Gaussian filters smooth the level set function and stabilises the evolution process. One of the merits of our proposed model stems from the ability to initialise the contour any where inside the image to extract object boundaries. The proposed method is found to perform well, notably when the intensities inside and outside the object are homogenous. Our method is applied with satisfactory results on various types of images, including synthetic, medical and Arabic-characters images.

Research paper thumbnail of A NOVEL GLOBAL THRESHOLD-BASED ACTIVE CONTOUR MODEL

In this paper, we propose a novel global threshold-based active contour model which employs a new... more In this paper, we propose a novel global threshold-based active contour model which employs a new edge-stopping function that controls the direction of the evolution and stops the evolving contour at weak or blurred edges. The model is implemented using selective binary and Gaussian filtering regularized level set (SBGFRLS) method. The method has a selective local or global segmentation property. It selectively penalizes the level set function to be a binary function. This is followed by using a Gaussian function to regularize it. The Gaussian filters smooth the level set function and afford the evolution more stability. The contour could be initialized anywhere inside the image to extract object boundaries. The proposed method performs well when the intensities inside and outside the object are homogenous. Our method is tested on synthetic, medical and Arabic characters images with satisfactory results

Research paper thumbnail of A MODIFIED DISTANCE REGULARIZED LEVEL SET MODEL FOR LIVER SEGMENTATION FROM CT IMAGES

Segmentation of organs from medical images is an active and interesting area of research. Liver s... more Segmentation of organs from medical images is an active and interesting area of research. Liver segmentation incurs more challenges and difficulties compared with segmentation of other organs. In this paper we demonstrate a liver segmentation method for computer tomography images. We revisit the distance regularization level set (DRLS) model by deploying new balloon forces. These forces control the direction of the evolution and slow down the evolution process in regions that are associated with weak or without edges. The newly added balloon forces discourage the evolving contour from exceeding the liver boundary or leaking at a region that is associated with a weak edge, or does not have an edge. Our experimental results confirm that the method yields a satisfactory overall segmentation outcome. Comparing with the original DRLS model, our model is proven to be more effective in handling oversegmentation problems.