Genetic Learning for Adaptive Image Segmentation
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Segmentation of resulting images is very important. Due to the growing importance of medical images for critical medical decisions, exploring technology to cancel human errors and getting the best image explains the necessity of studies in such a realm. In this paper, we investigate current techniques for MRI image segmentation and follow regional growth method for MRI image segmentation.
This method can also be used as an input for image analysis tools and software. As the size and number of medical images increase, using computers for faster processing and analysis becomes inevitable. In particular, computer algorithms are considered as a vocal part to describe anatomical structures in radiology automatization. Such algorithms are called image segmentation algorithms playing an important role in biomedical imaging applications such as determination of tissue volume[ 13 ], diagnosis[ 13 ], determination of pathological locations[ 14 ], studying anatomy[ 15 ], planning treatment[ 15 ], correcting partial volumetric data pertaining to and comprehensive surgery using computers[ 13 ].
For the reset, we explain some common methods offered by recent articles about medical image segmentation. So far, several methods have been developed. Some of them are 1-there is hold determination 2-region growth 3-classifiers 4-clustering 5-FCM 6 and active control. FCM was first introduced by[ 16 ] and improved by[ 17 ]. FCM algorithm is poor in working on important properties of images because neighboring pixels are interactive causing strong perturbation sensitivity.
To overcome such a weakness, a new classification algorithm called PCM is introduced in[ 18 ]. PCM moderates total pressure of columns in a fuzzy matrix and in traduces a probable partition matrix. PCM pays a cost on ignoring disorder points. First, it is sensitive to ignoring disorder points. Second, it is sensitive to disorder and often leads to the problem of adaptive classification.
On the other hand, potential membership is sensitive to selection of additional PCM parameters[ 19 ]. In this algorithm, statistical histogram of gray surface of image in KFCM algorithm is included for algorithm speed up as well as special information of image by KNN algorithm based on core methods in this method, spatial membership matrix between pixels and cones are constructed and then it is transcribed by membership matrix in traditional FCM algorithm which leads to some limitations. In what follows, we consider some methods for MRI segmentation[ 21 ].
Volume and intensity distribution are usually very complicated in medical images and threshold methods often fail. In most cases, threshold determination method is combined with other methods. One of these methods is segmentation based on threshold. In threshold method, a region is separated based on pixels with similar intensities. This method provides boundaries that separate objects from background based on their contrasts. Threshold method provides a binary output image from a gray image[ 22 ].
One of these methods is called general thresholding in which it is assumed that pixels of target and background have differences in gray surface and threshold is selected so that the target is separated from background. Another thresholding method is adaptive thresholding. This method is used when thresholding is not constant and the threshold is specified based on the location of target.
To select the threshold, there are various methods including selection of threshold with the help of histogram, clustering and iteration. In threshold determination methods, measurable images are divided into two types of intensities, it tries to separate desired classes by the value of intensity called threshold. Segmentation is resulted by classification of all pixels with higher values of threshold in one group and the rest of pixels in another group[ 23 ].
Threshold determination is a simple but effective tool to segment images in which various topologies have various separating intensities or other measurable properties. One segment is farmed interactively; however, there ore automatic methods. Threshold determination is often considered as early stage sequential image process. One of basic red tritons of this method is that in the simplest case of thresholding, only two classes are created that could not be used for multi-channel images.
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Furthermore, determination of threshold usually does not consider spatial properties of an image. This is why threshold determination method is sensitive to non-uniformity of noise intensity which is often present in MRI images[ 23 ]. For these reasons, diversity in threshold determination to segment medical images is proposed which records based on local intensity and data stream[ 24 ] threshold techniques are presented in[ 22 ].
In this method, region growth operation should be performed in each segment and there is the same issue of appropriate threshold determination for smoothness as threshold determination method.
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This method begins with the selection of one or more granular points and depending on the number of granular points, the same regions are specified. The similarities between pixels are formulated. This is a criterion based on the gray scale of pixels. Two pixels are similar in which absolute difference of their gray scale is lower than threshold value.
Then, region growth operation or monitoring and control of points enclosed in side granular points begin. If a point is similar enough to granular points, it will belong to that granular point[ 23 ]. For each region, the procedure continues until all available points in an image are covered. Region growth method is a technique to extract a region of an image that is connected based on some predefined criteria.
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In the simplest form, this method needs one grain that is selected by operator manually and all pixels connected to the grain are extracted with the same intensity initially[ 25 ]. This is shown in Figure 2 B where region growth method is used to extract one of its structures. As in threshold determination method, this method is not often used lonely. Rather, it is used in a series of image processing operations, in particular, to describe small and simple structures such as tumors and injuries[ 5 ].
Hence, for each region to be extracted, one grain needs to be implanted. Separation algorithms depend on region growth method but do not need granular implant. Region growth method is sensitive to noise because it results in holes of disconnection.
Genetic Learning for Adaptive Image Segmentation by Bir Bhanu, Sungkee Lee - htacesovmudde.ml
In contrast, negligible volumetric effects can lead to separated regions connected to each other. To overcome such problems, homo-topic growth algorithm is suggested to keep topology of initial and extracted regions. Artificial neural networks ANN are processing nodes that simulate biological information. Each node in ANN is capable of executing primary calculations.
Learned information is gained by adaption of assigned weights for connections among nodes. ANNs are an example of machine learning and could use in various types of image segmentation.
ANN can be used in inside monitoring methods such as classification method and also in change formable models. Due to huge number of interconnections used in neural network, spatial information could be used readily along with classification methods. Although ANN is inherently symmetric, processing is usually in series and hence its potential capabilities are decreased[ 25 ].
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In clustering method, data are divided into clusters in which the similarities between data inside each cluster are maximized and between different clusters are minimized. Clustering algorithms essentially play their roles as classification methods. Hence, these methods are called unsupervised methods. In order to compensate for the lack of educational data, classification methods are repeated between image segmentation and determination of properties of each class. On the one hand, classification methods self-educate accessible data and put these data accessible to the user on the other hand.
K-means clustering algorithm classifies data by calculating iterative average of intensity for each class and image segmentation through classification of each pixel of a class or the closet average[ 26 ]. The numbers of classes are assumed 3 including; cerebrospinal fluid, gray matter and white matter from dark gray to white, respectively. FCM algorithm is a generalization of k-means algorithm and provides the possibility of rough classifications based on fuzzy collection theories.
EM algorithm applies the same values as clustering algorithms with the following important assumption that data follow the Gaussian combination model[ 27 ]. This clustering method as introduced in[ 5 ] is based on minimization of target function. FCM optimizes the target function through updating membership function and center of clusters.
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This optimization process continues until reaching the threshold. FCM is widely used in medical image segmentation.