Intensity inhomogeneity or bias field in natural and medical images make image processing challenging. In this paper we have introduced a novel technique in which we first estimate the bias field using multi-scale filtering. Based on the bias field, the bias field corrected image is obtained which is used for accurate segmentation. For segmentation, a convex functional is proposed which is also suitable for multi-region segmentation. The proposed formulation is extended to vector-valued images and texture images. For comparison, the results are compared with state of the art models both qualitatively and quantitatively which validate strong enactment of the proposed formulation
Image segmentation is still an open problem especially when intensities of the objects of interest a...
This paper presents a variational level set method for simultaneous segmentation and bias field esti...
Abstract—This paper presents a novel variational approach for simultaneous estimation of bias field ...
This paper presents a novel active contour model in a variational level set formulation for simultan...
This study proposes a local bias field and difference estimation (LBDE) model for medical image segm...
Intensity nonuniformity is one of the common issues in image segmentation, which is caused by techni...
This paper presents a local- and global-statistics-based active contour model for image segmentation...
<div><p>This paper presents a region-based active contour method for the segmentation of intensity i...
Medical image segmentation finds application in computer-aided diagnosis, computer-guided surgery, m...
which permits unrestricted use, distribution, and reproduction in any medium, provided the original ...
Intensity inhomogeneity causes many difficulties in image segmentation and the under-standing of mag...
In MRI images Intensity inhomogeneity (IIH) occurs due to various factors which cause many difficult...
This paper represents a new region-based active contour model that can be used to segment images wit...
This paper presents a novel variational approach for simultaneous estimation of bias field and segme...
Abstract — Most of the image processing techniques use image regional information for image segmenta...
Image segmentation is still an open problem especially when intensities of the objects of interest a...
This paper presents a variational level set method for simultaneous segmentation and bias field esti...
Abstract—This paper presents a novel variational approach for simultaneous estimation of bias field ...
This paper presents a novel active contour model in a variational level set formulation for simultan...
This study proposes a local bias field and difference estimation (LBDE) model for medical image segm...
Intensity nonuniformity is one of the common issues in image segmentation, which is caused by techni...
This paper presents a local- and global-statistics-based active contour model for image segmentation...
<div><p>This paper presents a region-based active contour method for the segmentation of intensity i...
Medical image segmentation finds application in computer-aided diagnosis, computer-guided surgery, m...
which permits unrestricted use, distribution, and reproduction in any medium, provided the original ...
Intensity inhomogeneity causes many difficulties in image segmentation and the under-standing of mag...
In MRI images Intensity inhomogeneity (IIH) occurs due to various factors which cause many difficult...
This paper represents a new region-based active contour model that can be used to segment images wit...
This paper presents a novel variational approach for simultaneous estimation of bias field and segme...
Abstract — Most of the image processing techniques use image regional information for image segmenta...
Image segmentation is still an open problem especially when intensities of the objects of interest a...
This paper presents a variational level set method for simultaneous segmentation and bias field esti...
Abstract—This paper presents a novel variational approach for simultaneous estimation of bias field ...