<p>The stack is a preprocessed binary image and is volume-rendered with the color-map's alpha values of 0.2. The black ring is a large vessel. (A) The results of the CPCC points (light green point). (B) The seed points (red points) of touching cells selected from the 26 cubic neighbor points of CPCC. (C) The seed points (light yellow points) of sparse cells obtained by extracting the local maximum of the Gaussian-convoluted image. (D) Results of the CC-random walker segmentation. Different cells are labeled in unique random colors.</p
(A) Example phase image. (B) First step of automated seeding algorithm: Pre-processing and watershed...
<p>The top and bottom rows represent the K1 and K2 stacks. The stacks are preprocessed binary images...
In the image analysis, image segmentation is the operation that divides image into set of different ...
<p>The stack is a preprocessed binary image and volume-rendered with the color-map's alpha values of...
<p>A–E are volume rendered with the color-map's alpha values of 0.2. (A) The concave point detection...
<p>The stack is volume-rendered with the color-map's alpha values of 0.2. The three touching cells a...
<p>The light green dashed circle indicates the holes in the binarization step, and the light yellow ...
<p>(<b>A</b>) Original image. (<b>B</b>) Segmented layers by the k-means clustering segmentation. (<...
<p>(<b>A</b>) A phase image region around the previously selected seed is opened. (<b>B</b>) The pha...
<p>Original image (A), LoG filtered image with and (B, C), LoG scale-space maximum intensity proje...
<p>Segmentation results (red) for three different cases of proposed algorithm at the <i>axial</i> he...
Abstract- We present here a new algorithm for segmentation of intensity images which is robust, rapi...
With the adaptation of traditional image processing methods to answer to the requirements of analyzi...
Abstract. We present a method that automatically partitions a single image into non-overlapping regi...
Abstract For region growing image segmentation, seed selection and image noise are two major concern...
(A) Example phase image. (B) First step of automated seeding algorithm: Pre-processing and watershed...
<p>The top and bottom rows represent the K1 and K2 stacks. The stacks are preprocessed binary images...
In the image analysis, image segmentation is the operation that divides image into set of different ...
<p>The stack is a preprocessed binary image and volume-rendered with the color-map's alpha values of...
<p>A–E are volume rendered with the color-map's alpha values of 0.2. (A) The concave point detection...
<p>The stack is volume-rendered with the color-map's alpha values of 0.2. The three touching cells a...
<p>The light green dashed circle indicates the holes in the binarization step, and the light yellow ...
<p>(<b>A</b>) Original image. (<b>B</b>) Segmented layers by the k-means clustering segmentation. (<...
<p>(<b>A</b>) A phase image region around the previously selected seed is opened. (<b>B</b>) The pha...
<p>Original image (A), LoG filtered image with and (B, C), LoG scale-space maximum intensity proje...
<p>Segmentation results (red) for three different cases of proposed algorithm at the <i>axial</i> he...
Abstract- We present here a new algorithm for segmentation of intensity images which is robust, rapi...
With the adaptation of traditional image processing methods to answer to the requirements of analyzi...
Abstract. We present a method that automatically partitions a single image into non-overlapping regi...
Abstract For region growing image segmentation, seed selection and image noise are two major concern...
(A) Example phase image. (B) First step of automated seeding algorithm: Pre-processing and watershed...
<p>The top and bottom rows represent the K1 and K2 stacks. The stacks are preprocessed binary images...
In the image analysis, image segmentation is the operation that divides image into set of different ...