Image Segmentation Using Computational Intelligence Techniques: Review

Chouhan, Siddharth Singh; Kaul, Ajay; Singh, Uday Pratap

doi:10.1007/s11831-018-9257-4

Cited by 112 publications

(43 citation statements)

References 275 publications

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“…Methods that are used for extracting and representing the information from an image are termed segmentation techniques [4]. It serves as the pre-processing step for feature extraction.…”

Section: A Segmentationmentioning

confidence: 99%

Segmentation of Lungs in Chest Radiographs using Optimized Clustering Technique

V J*,

Krishnakumar

2020

IJRTE

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Lung Cancer is considered the most common type of disease, when compared to that of the other types. It is one of the leading types of cancer that causes majority of the patient death worldwide. It is often detected only at the later stage as they are usually diagnosed at their advanced stages. Survival of patients with lung cancer is almost impossible. They often die within one year after the onset of clinical symptoms. Screening and early detection play an important role in saving the life of a patient. Chest radiography and computerized tomography scans are the techniques mostly used to diagnose and detect tumor in lungs. They require less radiation dose, and is available in most of the diagnostic centers. Their cost is also less when compared to the other techniques used for diagnosis. Nodule detection by using conventional radiographs is still not much effective, so there arises a need for alternative image processing techniques to improve the efficiency of detection. Image segmentation is considered as the first step in processing an image. Further analysis of the image would be made more effective if segmentation is efficient. There exits many segmentation algorithms based on clustering and thresholding approaches. In this paper, a bimodal, optimized and modified k-means algorithm is developed to segment the chest images.

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“…Methods that are used for extracting and representing the information from an image are termed segmentation techniques [4]. It serves as the pre-processing step for feature extraction.…”

Section: A Segmentationmentioning

confidence: 99%

Segmentation of Lungs in Chest Radiographs using Optimized Clustering Technique

V J*,

Krishnakumar

2020

IJRTE

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“…DL is a special class of ML algorithms which have multiple layers for transforming the raw data into information. Eventually, it has been applied to solve several complex tasks like image classification, pattern analysis, feature extraction, and transformation [6], [7]. Authors have used this concept in various studies like, Chen et al in [8] have proposed a novel method using deep learning for counting the apples and oranges from the real-time images.…”

Section: Introductionmentioning

confidence: 99%

Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease

et al. 2019

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Fungal diseases not only influence the economic importance of the plants and its products but also abate their ecological prominence. Mango tree, specifically the fruits and the leaves are highly affected by the fungal disease named as Anthracnose. The main aim of this paper is to develop an appropriate and effective method for diagnosis of the disease and its symptoms, therefore espousing a suitable system for an early and cost-effective solution of this problem. Over the last few years, due to their higher performance capability in terms of computation and accuracy, computer vision, and deep learning methodologies have gained popularity in assorted fungal diseases classification. Therefore, for this paper, a multilayer convolutional neural network (MCNN) is proposed for the classification of the Mango leaves infected by the Anthracnose fungal disease. This paper is validated on a real-time dataset captured at the

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“…In this aspect of scale analysis, there is a gap in dense sampling features. The micro-time scale is an effective means of acquiring image spatial information, and its concept is very similar to human cognition of images [6]. For example, the concept of a tree only has practical significance in the range from a few centimeters to at most a few meters, and it is meaningless to discuss at the nanometer or kilometer level [7].…”

Section: Introductionmentioning

confidence: 99%

Virtual Reality Image Technology Assists Training Optimization of Motion Micro-Time Scale

Cao

Shang

2020

IEEE Access

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Virtual reality image local features are highly repetitive and are not easily affected by target occlusion, so research on virtual reality image local feature extraction techniques has received increasing attention. For the purpose of global scale selection, this article considers the virtual reality image in the global scope from the theoretical perspectives of the continuity of virtual reality images, the repeatability of virtual reality image features, the scale of scene virtual reality images in the global scope, and the visual saliency. This paper gives a general framework of multi-scale geometric analysis and particle swarm optimization virtual reality image matching algorithm. For the problem of virtual reality image matching, the original particle swarm optimization algorithm is improved. A more reasonable inertia weight change formula, a method for selecting the optimal solution when two objectives are optimized at the same time, and a method for selecting a reasonable input are designed. In this paper, performance evaluation experiments are performed on several feature description algorithms. The experimental results show that the descriptors constructed by these feature description algorithms are significantly more robust than SIFT (Scale Invariant Feature Transform) descriptors. The improved multi-scale geometric analysis and particle swarm optimization are robust due to the introduction of multiple support domains. It has the best discriminative ability, but at the same time, the introduction of multiple support domains leads to the worst real-time algorithm; DGOH (Dual Gradient Orientation Histogram) algorithm is more robust than FRDOH (Fast Representation Based on a Double Orientation Histogram) and SIFT algorithm, and DGOH algorithm has the best real-time performance. INDEX TERMS virtual reality images; particle swarm optimization; multi-scale geometric analysis; inertial weights; feature description.

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Image Segmentation Using Computational Intelligence Techniques: Review

Cited by 112 publications

References 275 publications

Segmentation of Lungs in Chest Radiographs using Optimized Clustering Technique

Segmentation of Lungs in Chest Radiographs using Optimized Clustering Technique

Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease

Virtual Reality Image Technology Assists Training Optimization of Motion Micro-Time Scale

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