scholarly journals Combination of Color and Focus Segmentation for Medical Images with Low Depth-of-Field

2018 ◽  
Vol 4 (1) ◽  
pp. 345-349
Author(s):  
Tamara Wirth ◽  
Ady Naber ◽  
Werner Nahm
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Medical Images ◽  
Image Data ◽  
Image Understanding ◽  
Surgical Instruments ◽  
Second Step ◽  
Depth Of Field ◽  
Segmentation Methods ◽  
Edge Based

AbstractImage segmentation plays an increasingly important role in image processing. It allows for various applications including the analysis of an image for automatic image understanding and the integration of complementary data. During vascular surgeries, the blood flow in the vessels has to be checked constantly, which could be facilitated by a segmentation of the affected vessels. The segmentation of medical images is still done manually, which depends on the surgeon’s experience and is time-consuming. As a result, there is a growing need for automatic image segmentation methods. We propose an unsupervised method to detect the regions of no interest (RONI) in intraoperative images with low depth-of-field (DOF). The proposed method is divided into three steps. First, a color segmentation using a clustering algorithm is performed. In a second step, we assume that the regions of interest (ROI) are in focus whereas the RONI are unfocused. This allows us to segment the image using an edge-based focus measure. Finally, we combine the focused edges with the color RONI to determine the final segmentation result. When tested on different intraoperative images of aneurysm clipping surgeries, the algorithm is able to segment most of the RONI not belonging to the pulsating vessel of interest. Surgical instruments like the metallic clips can also be excluded. Although the image data for the validation of the proposed method is limited to one intraoperative video, a proof of concept is demonstrated.

scholarly journals Segmentation of image based on k-means and modified subtractive clustering

2021 ◽  
Vol 22 (3) ◽  
pp. 1396
Author(s):  
Simon Tongbram ◽  
Benjamin A. Shimray ◽  
Loitongbam Surajkumar Singh
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Medical Science ◽  
Cluster Center ◽  
Subtractive Clustering ◽  
Cluster Centroid ◽  
Segmentation Methods ◽  
Segmentation Approach ◽  
Data Points

Image segmentation has widespread applications in medical science, for example, classification of different tissues, identification of tumors, estimation of tumor size, surgery planning, and atlas matching. Clustering is a widely implemented unsupervised technique used for image segmentation mainly because of its simplicity and fast computation. However, the quality and efficiency of clustering-based segmentation is highly depended on the initial value of the cluster centroid. In this paper, a new hybrid segmentation approach based on k-means clustering and modified subtractive clustering is proposed. K-means clustering is a very efficient and powerful algorithm but it requires initialization of cluster centroid. And, the consistency of the clustering outcomes of k-means algorithm depends on the initial selection of the cluster center. To overcome this drawback, a modified subtractive clustering algorithm based on distance relations between cluster centers and data points is proposed which finds a more accurate cluster centers compared to the conventional subtractive clustering. These cluster centroids obtained from the modified subtractive clustering are used in k-means algorithm for segmentation of the image. The proposed method is compared with other existing conventional segmentation methods by using several synthetic and real images and experimental finding validates the superiority of the proposed method.

scholarly journals SMBFT: A Modified Fuzzy c -Means Algorithm for Superpixel Generation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhen Yu ◽  
Cuihuan Tian ◽  
Shiyong Ji ◽  
Benzheng Wei ◽  
Yilong Yin
Keyword(s):  
Clustering Algorithm ◽  
Spatial Information ◽  
Fuzzy Theory ◽  
Image Data ◽  
Natural Images ◽  
Data Sets ◽  
Mr Images ◽  
Negative Effects ◽  
Maximum Probability ◽  
Segmentation Methods

Most traditional superpixel segmentation methods used binary logic to generate superpixels for natural images. When these methods are used for images with significantly fuzzy characteristics, the boundary pixels sometimes cannot be correctly classified. In order to solve this problem, this paper proposes a Superpixel Method Based on Fuzzy Theory (SMBFT), which uses fuzzy theory as a guide and traditional fuzzy c -means clustering algorithm as a baseline. This method can make full use of the advantage of the fuzzy clustering algorithm in dealing with the images with the fuzzy characteristics. Boundary pixels which have higher uncertainties can be correctly classified with maximum probability. The superpixel has homogeneous pixels. Meanwhile, the paper also uses the surrounding neighborhood pixels to constrain the spatial information, which effectively alleviates the negative effects of noise. The paper tests on the images from Berkeley database and brain MR images from the Brain web. In addition, this paper proposes a comprehensive criterion to measure the weights of two kinds of criterions in choosing superpixel methods for color images. An evaluation criterion for medical image data sets employs the internal entropy of superpixels which is inspired by the concept of entropy in the information theory. The experimental results show that this method has superiorities than traditional methods both on natural images and medical images.

scholarly journals Adaptive Image Segmentation for Traumatic Brain Haemorrhage

TEM Journal ◽  
2021 ◽  
pp. 1476-1487
Author(s):  
Ahmad Yahya Dawod ◽  
Aniwat Phaphuangwittayakul
Keyword(s):  
Image Segmentation ◽  
Clinical Diagnostics ◽  
Accuracy Rate ◽  
Brain Images ◽  
Computational Costs ◽  
Contour Model ◽  
Segmentation Methods ◽  
Edge Based ◽  
Performance Results ◽  
Brain Haemorrhage

It is challenging to establish a significant solution with computer techniques to improve the speed and efficiency of Traumatic Brain Injury (TBI) diagnosis. Several segmentation methods involving diverse precision and a degree of effort have been proposed and detailed within the related literature. Segmentation of Brain image is one of the significant clinical diagnostics implements. This paper proposes a modified (MDRLSE) calculation for haemorrhage segmentation on Computed Tomography (CT) images. The image noise that abdicates the obscured edges is utilized to portray the precise boundary of the haemorrhage region. The proposed segmentation technique achieved an accuracy rate of 97.16%. The technique is implemented using an edge-based involved contour model for image segmentation, providing a simple narrowband to significantly reduce computational costs. The performance results show that it is effective for TBI image segmentation in brain images with various characteristics.

Two-Step Modified Nash Equilibrium Method for Medical Image Segmentation Based on an Improved C-V Model

2018 ◽  
Vol 8 (9) ◽  
pp. 1826-1834
Author(s):  
Tian Chi Zhang ◽  
Jian Pei Zhang ◽  
Jing Zhang ◽  
Melvyn L. Smith
Keyword(s):  
Image Segmentation ◽  
Nash Equilibrium ◽  
Level Set ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Clustering Problem ◽  
Object Contour ◽  
Segmentation Methods ◽  
Maximum Similarity ◽  
Segmentation Problem

One of the most established region-based segmentation methods is the region based C-V model. This method formulates the image segmentation problem as a level set or improved level set clustering problem. However, the existing level set C-V model fails to perform well in the presence of noisy and incomplete data or when there is similarity between the objects and background, especially for clustering or segmentation tasks in medical images where objects appear vague and poorly contrasted in greyscale. In this paper, we modify the level set C-V model using a two-step modified Nash equilibrium approach. Firstly, a standard deviation using an entropy payoff approach is employed and secondly a two-step similarity clustering based approach is applied to the modified Nash equilibrium. One represents a maximum similarity within the clustered regions and the other the minimum similarity between the clusters. Finally, an improved C-V model based on a two-step modified Nash equilibrium is proposed to smooth the object contour during the image segmentation. Experiments demonstrate that the proposed method has good performance for segmenting noisy and poorly contrasting regions within medical images.

scholarly journals Segmenting radiographs of the hand and wrist using computer vision

1990 ◽  
Author(s):  
Γεώργιος Μάνος
Keyword(s):  
Image Segmentation ◽  
Region Growing ◽  
Bone Age ◽  
Growth Disorders ◽  
Region Merging ◽  
Clinical Tool ◽  
Age Assessment ◽  
Segmentation Methods ◽  
Bone Age Assessment ◽  
Edge Based

"Bone age" age assessment is an important clinical tool in the area of paediatrics. The technique is based upon the appearance and growth of specific bones in a developing child. In particular most methods for "bone age" assessment are based on the examination of the growth of bones of the left hand and wrist on X-ray films. This assessment is useful in the treatment of growth disorders and also is used to predict adult height. One of the most reliable methods for "bone age" assessment is the TW2 method. The drawback of this method is that it is time consuming and therefore its automation is highly desirable. One of the most important aspects of the automation process is image segmentation i.e. the extraction of bones from soft-tissue and background. Over the past 10 years various attempts have been made at the segmentation of handwrist radiographs but with limited success. This can mainly be attributed to the characteristics of the scenes e.g. biological objects, penetrating nature of radiation, faint bone boundaries, uncertainty of scene content, and conjugation of bones. Experience in the field of radiographic image analysis has shown thatsegmentation of radiographic scenes is a difficult task requiring solutions which depend on the nature of the particular problem.There are two main approaches to image segmentation: edge based and region based. Most of the previous attempts at the segmentation of hand-wrist radiographs were edge based. Edge based methods usually require a w-ell defined model of the object boundaries in order to produce successful results. However, for this particular application it is difficult to derive such a model. Region based segmentation methods have produced promising results for scenes which exhibit uncertainty regarding their content and boundaries of objects in the image, as in the case, for example, of natural senes. This thesis presents a segmentation method based on the concept of regions. This method consists of region growing and region merging stages. A technique was developed for region merging which combines edge and region boundai^ information. A bone extraction stage follows which labels regions as either boneor background using heuristic rules based on the grey-level properties of the scene. Finally, a technique is proposed for the segmentation of bone outlines which helps in identifying conjugated bones. Experimental results have demonstrated that this method represents a significant improvement over existing segmentation methods for hand-wrist radiographs, particularly with regard to the segmentation of radiographs with varying degrees of bone maturity.

An Automatic Magnetic Resonance Brain Image Segmentation Method Using a Multitask Weighted Fuzzy Clustering Algorithm

2021 ◽  
Vol 11 (8) ◽  
pp. 2177-2183
Author(s):  
Lei Hua ◽  
Jing Xue ◽  
Kaijian Xia ◽  
Leyuan Zhou ◽  
Pengjiang Qian ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Brain Images ◽  
Brain Image ◽  
Fuzzy Clustering Algorithm ◽  
Brain Image Segmentation

In clinical assisted diagnosis, it is an important way to obtain information with the help of medical images. Qualitative and quantitative analysis of brain tissue has become a research hotspot for brain diseases. Therefore, image segmentation technology is an indispensable link in medical image analysis. Due to the defects such as ambiguity, complexity, gray-scale unevenness, partial volume effect in magnetic resonance brain images, it is essential to improve the segmentation performance of classical algorithms in medical images. In this paper, multitasking and weighted fuzzy clustering algorithm are combined as a new algorithm (MT-WFCM) for MRI brain image segmentation. The proposed MT-WFCM algorithm improve the clustering performance of all tasks through common information between different magnetic resonance brain images with correlation. Besides, the difference between MT-WFCM and MT-FCM is that task weights are added to avoid negative effects between tasks in the segmentation process. According to five different comparative experiments, the MT-WFCM algorithm can mine the cooperative relationship among each task and the characteristics of each task effectively. In magnetic resonance image (MRI) segmentation, multi-task weighted fuzzy c-means clustering method can make up for the shortcomings of single-task clustering algorithm, strengthen the relationship between tasks, and get more accurate segmentation results.

MRI SEGMENTATION BY FUZZY CLUSTERING METHOD BASED ON PRIOR KNOWLEDGE

2016 ◽  
Vol 28 (04) ◽  
pp. 1650025
Author(s):  
Mahsa Badiee Yazdi ◽  
Mohammad Mahdi Khalilzadeh ◽  
Mohsen Foroughipour
Keyword(s):  
Image Segmentation ◽  
White Matter ◽  
Prior Knowledge ◽  
Gray Matter ◽  
Clustering Algorithm ◽  
Similarity Index ◽  
Noise Levels ◽  
Fcm Algorithm ◽  
Segmentation Methods ◽  
Fuzzy Clustering Method

Image segmentation is often required as a fundamental stage in medical image processing, particularly during the clinical analysis of magnetic resonance (MR) brain images. Fuzzy c-means (FCM) clustering algorithm is one of the best known and widely used segmentation methods, but this algorithm has some problem for segmenting simulated MRI images to high number of clusters with different noise levels and real images because of spatial complexities. Anatomical segmentation usually requires information derived from the manual segmentations done by experts, prior knowledge can be useful to modify image segmentation methods. In this paper, we propose some methods to modify FCM algorithm using expert manual segmentation as prior knowledge. We developed combination of FCM algorithm and prior knowledge in three ways, in order to improve segmentation of brain MR images with high noise level and spatial complexity. In real images, we had a considerable improvement in similarity index of three classes (white matter, gray matter, CSF) and in simulated images with different noise levels evaluation criteria of white matter and gray matter has improved.

Visual Medical Information Analysis

2011 ◽  
pp. 4034-4040
Author(s):  
Maria Papadogiorgaki ◽  
Vasileios Mezaris ◽  
Yiannis Chatzizisis
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Visual Information ◽  
Medical Image ◽  
Medical Images ◽  
Clinical Analysis ◽  
Medical Image Segmentation ◽  
Medical Imagery ◽  
Segmentation Methods ◽  
Magnetic Resonance Imaging Mri

Images have constituted an essential data source in medicine in the last decades. Medical images derived from diagnostic technologies (e.g., X-ray, ultrasound, computed tomography, magnetic resonance, nuclear imaging) are used to improve the existing diagnostic systems for clinical purposes, but also to facilitate medical research. Hence, medical image processing techniques are constantly investigated and evolved. Medical image segmentation is the primary stage to the visualization and clinical analysis of human tissues. It refers to the segmentation of known anatomic structures from medical images. Structures of interest include organs or parts thereof, such as cardiac ventricles or kidneys, abnormalities such as tumors and cysts, as well as other structures such as bones, vessels, brain structures and so forth. The overall objective of such methods is referred to as computer-aided diagnosis; in other words, they are used for assisting doctors in evaluating medical imagery or in recognizing abnormal findings in a medical image. In contrast to generic segmentation methods, techniques used for medical image segmentation are often applicationspecific; as such, they can make use of prior knowledge for the particular objects of interest and other expected or possible structures in the image. This has led to the development of a wide range of segmentation methods addressing specific problems in medical applications. In the sequel of this article, the analysis of medical visual information generated by three different medical imaging processes will be discussed in detail: Magnetic Resonance Imaging (MRI), Mammography, and Intravascular Ultrasound (IVUS). Clearly, in addition to the aforementioned imaging processes and the techniques for their analysis that are discussed in the sequel, numerous other algorithms for applications of segmentation to specialized medical imagery interpretation exist.

White Patch Detection in Brain MRI Image Using Evolutionary Clustering Algorithm

2016 ◽  
pp. 323-339
Author(s):  
Pradeep Kumar Mallick ◽  
Mihir Narayan Mohanty ◽  
S. Saravana Kumar
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Brain Mri ◽  
Medical Diagnostics ◽  
Vital Role ◽  
Evolutionary Clustering ◽  
Edge Detectors ◽  
Ct Brain ◽  
Edge Based ◽  
The Brain

Though image segmentation is a fundamental task in image analysis; it plays a vital role in the area of image processing. Its value increases in case of medical diagnostics through medical images like X-ray, PET, CT and MRI. In this paper, an attempt is taken to analyse a CT brain image. It has been segmented for a particular patch in the brain CT image that may be one of the tumours in the brain. The purpose of segmentation is to partition an image into meaningful regions with respect to a particular application. Image segmentation is a method of separating the image from the background, read the contents and isolating it.In this paper both the concept of clustering and thresholding technique with edge based segmentation methods like sobel, prewitt edge detectors is applied. Then the result is optimized using GA for efficient minimization of the objective function and for improved classification of clusters. Further the segmented result is passed through a Gaussian filter to obtain a smoothed image.

A Novel Video Detection System on Traffic Flow Inspection

2012 ◽  
Vol 182-183 ◽  
pp. 440-444
Author(s):  
Zhan Wen Liu ◽  
Shan Lin ◽  
Sheng Gen Dou
Keyword(s):  
Image Segmentation ◽  
Traffic Flow ◽  
Clustering Algorithm ◽  
Dynamic Range ◽  
Detection System ◽  
Image Data ◽  
Image Sensor ◽  
Imaging Device ◽  
The Core ◽  
Video Detection

A prototype of video detection system applied to traffic flow inspection is developed, which uses CMOS linear image sensor with high resolution 2K pixels and wide dynamic range as the core of imaging device. It combines FPGA with DSP as the core of acquisition and processing of massive image data. Moreover, a novel multiscale and hierarchical clustering algorithm for image segmentation is presented. Based on the theory of graph spectral, the algorithm can construct a new graph by analyzing the feature of an original image at different clustering scales, so that image segmentation can be accomplished easily to segment the image. The simulation results show that the row scan speed of this system can reach to 1000 lines per second, the resolution being 2048 pixels.

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