Medical Image Processing and Analysis

Tian, Jie; Wang, Yuanyuan; Dai, Xiaoqian; Zhang, Xing

doi:10.1007/978-3-642-34303-2_11

Cited by 11 publications

(7 citation statements)

References 57 publications

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“…Extending the Otsu method to double threshold segmentation, the optimal threshold value 1 t , 2 t , and they should get the maximum variance [1], that is In this way, using the between-cluster variance method, the double threshold image segmentation threshold of solving problem can be summarized as the best threshold value, the optimization problem.…”

Section: The Otsu Threshold Methodsmentioning

confidence: 99%

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Image Edge Detection Algorithm Based on The Parallel Genetic and Otsu dual-threshold

Dang¹,

Li²

2015

Advances in Intelligent Systems Research

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Abstract. The traditional genetic algorithm is used to search some function extreme value, but it has low precision and poor stability. For this shortage, A kind of parallel genetic algorithm based on Otsu double threshold algorithm of image edge detection, the design into a line of two columns of the vector population, than as an individual's fitness variance between, generated a number of initial population of parallel computing, At the same time, the largest fitness individuals in each generation directly copied into the next generation, evolution in a number of different subgroup in parallel, in order to avoid the premature convergence phenomenon appeared in the process of single population evolution, to improve the algorithm convergence speed. The experimental results show that the algorithm is compared with the traditional genetic algorithm, can more accurate and has better accuracy and stability.

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Section: The Otsu Threshold Methodsmentioning

confidence: 99%

“…)、template matching、optimal curved surface fitting, etc. [1].These algorithms have made some achievements in improving edge detection effect, However, they have complicated mathematical model and a longer run time also.…”

Section: Introductionmentioning

confidence: 99%

Image Edge Detection Algorithm Based on The Parallel Genetic and Otsu dual-threshold

Dang¹,

Li²

2015

Advances in Intelligent Systems Research

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“…However, issues including uneven datasets, a lack of labeled data, and the interpretability of deep learning models continue to be major barriers in this discipline. Future studies should concentrate on overcoming the difficulties involved in detecting and analyzing brain tumors using machine learning [11]. The incorporation of multimodal imaging data, the creation of hybrid models incorporating various algorithms [12], and the use of cutting-edge methods like explainable AI and transfer learning show promise for enhancing early tumour identification and precise analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Algorithm for Early Detection and Analysis of Brain Tumors Using MRI Images

Terdale

Bhole

Bhor

et al. 2023

IJRITCC

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Among the human body's organs, the brain is the most delicate and specialized. It is proven that after the heart stops then also brain death occurs within 3 to 5 minutes of death or within 3 to 5 minutes of loss of oxygen supply. A brain tumor is a life-threatening disease that can be detected at any age from an infant to an old person. Though a lot of people did research in the detection and analysis of a tumor, but then also detecting tumors at the early phase is still a much more arduous field in the biomedical study. This paper focuses on the comparative study of various existing algorithms in this field. This paper addresses the challenges and some issues in MRI brain tumor detection which are also addressed in this research.

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“…The first is time-domain imaging algorithms, represented by the Back Projection (BP) and Fast Factorized Back Projection (FFBP) algorithms [22]. The second is frequency domain imaging algorithms, represented by the wavefront reconstruction imaging method [23] and the frequency domain imaging algorithm based on sub-aperture [24]. The wavefront reconstruction algorithm uses a fast Fourier transform (FFT) to improve the operation efficiency, but the inversion of the system kernel function matrix increases the complexity of this algorithm [5].…”

Section: Introductionmentioning

confidence: 99%

A Sub-Aperture Overlapping Imaging Method for Circular Synthetic Aperture Radar Carried by a Small Rotor Unmanned Aerial Vehicle

Chu,

Ma,

et al. 2023

Sensors

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Circular synthetic aperture radar (CSAR) can obtain higher image resolution and more target information using 360° observation of the target. Due to the anisotropy of target scattering characteristics in the actual scene, the sub-aperture imaging method is usually used for CSAR imaging. However, the uniformly divided overlapping sub-aperture CSAR imaging algorithm only considers phase compensation, ignoring the effect of target scattering characteristics on echo amplitude. In CSAR imaging scenarios carried by small rotor unmanned aerial vehicles (SRUAVs), the size of the observed scene cannot be ignored compared to the distance between the target and the antenna and the effect of the anisotropy of the target scattered energy on the echo amplitude should be considered. In this paper, a sub-aperture CSAR imaging method based on adaptive overlapping sub-aperture is proposed. First, the boundary points of the sub-aperture are determined by analyzing the correlation coefficient and the variation coefficient of the energy function. Next, the overlapping sub-aperture division schemes are automatically generated by screening and combining the boundary points. The sub-aperture images are then generated by a Back Projection (BP) algorithm. Finally, sub-aperture image registration and incoherent superposition are used to generate the final CSAR image. Verified by the CSAR field echo data, the proposed method can realize imaging of the original echo data without the Inertial Navigation System (INS) and Global Positioning System (GPS) observation data. Compared with the CSAR full-aperture BP imaging algorithm, the entropy of the image generated by the proposed method increased by 66.77%. Compared with the sub-aperture CSAR imaging algorithm, the entropy of the image generated by the proposed method was improved by 11.12%, retaining more details of the target, improving the target contour features, and enhancing the focusing effect.

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Medical Image Processing and Analysis

Cited by 11 publications

References 57 publications

Image Edge Detection Algorithm Based on The Parallel Genetic and Otsu dual-threshold

Image Edge Detection Algorithm Based on The Parallel Genetic and Otsu dual-threshold

Machine Learning Algorithm for Early Detection and Analysis of Brain Tumors Using MRI Images

A Sub-Aperture Overlapping Imaging Method for Circular Synthetic Aperture Radar Carried by a Small Rotor Unmanned Aerial Vehicle

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