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Image Thresholding using Histogram Fuzzy Approximation
Abstract
Image segmentation is one of the most important techniques in image processing. It is widely used in different applications such as computer vision, digital pattern recognition, robot vision, etc. Histogram was the earliest feature that has been used for isolating objects from their background, it is widely applicable in different application in which one needs to divide the image into distinct regions like background and object. The thresholding technique is the most popular solution in which a value on the histogram is selected to separate the regions. This value, which is known as the threshold, should be specified in an appropriate way. One of the methods is by using the global minimum value of the histogram and divides the histogram into white and black (binary image). Due to the spatial and grey uncertainty and ambiguity, the extraction of the threshold value in a crispy way is not suitable always. To overcome such problems, the proposed method uses two membership functions to measure the whiteness and blackness of a member element. The pixel belonging to one of the region is dependent on the membership value it has according to the membership functions.
... The regions in the resultant image are divided into two types, salient ℝ and non-salient ℝ , where ℝ = ℝ ∪ ℝ . The fuzzy bimodal thresholding technique (FBMT) can be used since the saliency is not well defined and fuzzy [43]. In FBMT the histogram is approximated by two fuzzy membership functions based on the peaks of the histogram. ...
Saliency extraction is a technique inspired by the human approach in processing a selected portion of the visual information received. This feature in the human visual system helps reduce the processing the brain needs to extract important information and neglect general and unimportant information. This paper presents a novel approach to identifying the saliency of regions in a scene from which objects likely to be salient can be extracted. The proposed approach uses two stages, namely, local saliency identification (LSI) and global saliency identification (GSI) and uses irregularity as the saliency measure in both stages. Local saliency uses the structure of the object to determine saliency while global saliency identifies the saliency of the region based on the contrast in relation to the entire background. An object is considered to be salient if it satisfies both local and global criteria. In this work, the key challenges and limitations of existing methods, such as the sensitivity to texture and noise, the need to manually define certain parameters, and the need to have pre-knowledge of the nature of the image, were considered and appropriate solutions have been suggested. The proposed algorithm was tested on a set of 1000 images selected from MSRA saliency identification standard dataset and benchmarked with state-of-the-art approaches. The results obtained showed very good efficiency and this is evident from the evaluation values obtained from the used evaluation method, e. g. the value of the F-measure, reached 96.5 per cent in some cases. The limitation of the approach was with complex objects which themselves comprising more than one important region such as an image of a person. This will be discussed thoroughly in the result section.
... ( ) which measure the white membership value and ( )which measure how much is the pixel black [26].The value of T can be found in different ways of which is the intersection of the two lines of the membership functions. The membership function can be derived from the Fig. 4(b) is as follow: ...
Tumor boundary detection is one of the challenging tasks in the medical diagnosis field. The proposed work constructed brain tumor boundary using bi-modal fuzzy histogram thresholding and edge indication map (EIM). The proposed work has two major steps. Initially step 1 is aimed to enhance the contrast in order to make the sharp edges. An intensity transformation is used for contrast enhancement with automatic threshold value produced by bimodal fuzzy histogram thresholding technique. Next in step 2 the EIM is generated by hybrid approach with the results of existing edge operators and maximum voting scheme. The edge indication map produces continuous tumor boundary along with brain border and substructures (cerebrospinal fluid (CSF), sulcal CSF (SCSF) and interhemispheric fissure) to reach the tumor location easily. The experimental results compared with gold standard using several evaluation parameters. The results showed better values and quality to proposed method than the traditional edge detection techniques. The 3D volume construction using edge indication map is very useful to analysis the brain tumor location during the surgical planning process.
Index Terms—Medical imaging, brain tumor, fuzzy histogram, edge indication map, 3D volume construction
... Several algorithms have been widely proposed in the literature for the bi-level [17,18,19,20,21] and also for the multi-level thresholding problem. For two level thresholding, solving the problem is same as finding the threshold value called T which satisfies this condition: pixels which are lower than T represent the object and the other pixels the background. ...
Background: Among the brain-related diseases, brain tumor segmentation on magnetic resonance imaging (MRI) scans is one of the highly focused research domains in the medical community. Brain tumor segmentation is a very challenging task due to its asymmetric form and uncertain boundaries. This process segregates the tumor region into the active tumor, necrosis and edema from normal brain tissues such as white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF).
Introduction: The proposed paper analyzed the advancement of brain tumor segmentation from conventional image processing techniques, to deep learning through machine learning on MRI of human head scans.
Method: State-of-the-art methods of these three techniques are investigated, and the merits and demerits are discussed.
Results: The prime motivation of the paper is to instigate the young researchers towards the development of efficient brain tumor segmentation techniques using conventional and recent technologies.
Conclusion: The proposed analysis concluded that the conventional and machine learning methods were mostly applied for brain tumor detection, whereas deep learning methods were good at tumor substructures segmentation.
This research introduces an image retrieval system which is, in different ways, inspired by the human vision system. The main problems with existing machine vision systems and image understanding are studied and identified, in order to design a system that relies on human image understanding. The main improvement of the developed system is that it uses the human attention principles in the process of image contents identification. Human attention shall be represented by saliency extraction algorithms, which extract the salient regions or in other words, the regions of interest. This work presents a new approach for the saliency identification which relies on the irregularity of the region. Irregularity is clearly defined and measuring tools developed. These measures are derived from the formality and variation of the region with respect to the surrounding regions. Both local and global saliency have been studied and appropriate algorithms were developed based on the local and global irregularity defined in this work.
The need for suitable automatic clustering techniques motivate us to study the available clustering techniques and to development of a technique that is suitable for salient points clustering. Based on the fact that humans usually look at the surrounding region of the gaze point, an agglomerative clustering technique is developed utilising the principles of blobs extraction and intersection. Automatic thresholding was needed in different stages of the system development. Therefore, a Fuzzy thresholding technique was developed.
Evaluation methods of saliency region extraction have been studied and analysed; subsequently we have developed evaluation techniques based on the extracted regions (or points) and compared them with the ground truth data.
The proposed algorithms were tested against standard datasets and compared with the existing state-of-the-art algorithms. Both quantitative and qualitative benchmarking are presented in this thesis and a detailed discussion for the results has been included. The benchmarking showed promising results in different algorithms. The developed algorithms have been utilised in designing an integrated saliency-based image retrieval system which uses the salient regions to give a description for the scene. The system auto-labels the objects in the image by identifying the salient objects and gives labels based on the knowledge database contents. In addition, the system identifies the unimportant part of the image (background) to give a full description for the scene.
Saliency or salient region extraction from images is still a challenging field as it needs some understanding of the image and its nature. A technique that is suitable for some applications is not necessarily useful in other applications, thus, saliency identification is dependent upon the application. Based on a survey of existing methods of saliency detection, a new technique to extract the salient regions from an image is proposed that utilizes local features of the region surrounding each pixel. The level of saliency is decided based on the irregularity of the region with compared to other regions. To make the process fully automatic, a new Fuzzy-based thresholding technique has also been developed. In addition to the above, a survey of existing saliency evaluation techniques has been carried out and we have proposed new evaluation methods. The proposed saliency extraction technique has been compared with other algorithms reported in the literature, and the results are discussed in detail.
In the area of image processing, segmentation of an image into multiple regions is very important for classification and recognition steps. It has been widely used in many application fields such as medical image analysis to characterize and detect anatomical structures, robotics features extraction for mobile robot localization and detection and map procession for lines and legends finding. Many techniques have been developed in the field of image segmentation. Methods based on intelligent techniques are the most
used such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO) called metaheuristics algorithms. In this paper, we describe a novel method for segmentation of images based on one of the most popular and efficient metaheuristic algorithm called Particle Swarm optimization (PSO) for determining multilevel threshold for a given image. The proposed method takes advantage of the characteristics of the particle swarm optimization and improves the objective function value to updating the velocity and the position of particles. This method is compared to the basic PSO method, also, it is compared with other known multilevel segmentation methods to demonstrate its efficiency. Experimental results show that this method can reliably segment and give threshold values than other methods considering different measures.
Abstract— Image segmentation is one of the most important
techniques in image processing, it is widely used in variety of
applications like; computer vision, digital pattern recognition,
robot vision, etc. histogram was the earliest feature which has
been used for segmenting objects from their background, it is
widely applicable in different application in which one needs to
divide the image into two distinct regions like background and
object. The thresholding technique is the most popular solution
in which a value on the histogram is selected to separate the two
regions. This value which is called the threshold should be
specified in an appropriate way. One of the methods is by using
the global minimum value of the histogram and divides the
histogram to white and black (binary image). Due to the spatial
and gray uncertainty and ambiguity the image used to contain,
crispy selection of the threshold value is not suitable always, to
overcome such problem the proposed method uses two
membership function to measure the whiteness and blackness of
a member element. The pixel belonging to one of the region is
dependent on the membership value it has according to the
membership functions.
Index Terms— Image segmentation, bimodal histogram, fuzzy
intelligence, thresholding.
Saliency or Salient regions extractions form
images is still a challenging field since it needs some
understanding for the image and the nature of the image.
The technique that is suitable in some application is not
necessarily useful in other application, thus, saliency
enhancement is application oriented. In this paper, a new
technique of extracting the salient regions from an image is
proposed which utilizes the local features of the surrounding
region of the pixels. The level of saliency is then decided
based on the global comparison of the saliency-enhanced
image. To make the process fully automatic a new Fuzzy-
Based thresholding technique has been proposed also. The
paper contains a survey of the state-of-the-art methods of
saliency evaluation and a new saliency evaluation technique
was proposed.
Image segmentation and object extraction plays an important role in image analysis and computer vision. In this paper, we propose a novel technique for color image segmentation called 'adaptive neuro-fuzzy color image seg-mentation (ANFCIS)'.The proposed system consists of multilayer perceptron (MLP)-like network which performs color image segmentation using multilevel thresholding. Threshold values for detecting clusters and their labels are found automatically using fuzzy C-means (FCM) clustering technique. Fuzzy entropy is used as a tool to de-cide the number of clusters. ANFCIS uses saturation and intensity planes of HSV (hue, saturation, intensity) color space for segmentation. Neural network is employed to find the number of objects automatically from an image. The major advantage of this method is that it does not require a priori knowledge to segment a color image. The algorithm is found to be robust and relatively computationally inexpensive for large variety of color images. Ex-perimental results have demonstrated the effectiveness and efficiency of the proposed method.