A hardware Implementation of OTSU Thresholding Method for Skin Cancer Image Segmentation

Khoukhi, Hasnae El; Filali, Youssef; Yahyaouy, Ali; Sabri, My Abdelouahed; Aarab, Abdellah

doi:10.1109/wits.2019.8723815

Cited by 12 publications

(7 citation statements)

References 9 publications

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“…First, the sky and nonsky regions are segmented by OTSU image segmentation method [ 31 – 34 ], and the average intensity value of the sky region of the original image after segmentation is taken as the estimated value of A . Since the depth of field of the sky region can be regarded as infinite, that is d ( x )⟶+ ∞ , we can know t ( x ) ≈ 0, taking it into equation ( 3 ), It can be seen that:

…”

Section: New Image Defogging Algorithmmentioning

confidence: 99%

Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

Sun

Wang

et al. 2022

Journal of Environmental and Public Health

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According to the characteristics of foggy images, such as high noise, low resolution, and uneven illumination, an improved foggy image enhancement method based on dark channel priority is proposed. First, the new algorithm refines the transmittance and optimizes the atmospheric light value and converts the restored image to HSV space. Second, the brightness V component is enhanced by MSRCR algorithm improved by bilateral filtering, and the saturation S is improved by adaptive stretching algorithm. Finally, the image is converted from HSV space to RGB space to complete image enhancement. The new method solves the problems of that the color of large area is uneven and the overall color of the image is dark when the traditional dark channel prior method is used to remove fog. The experimental results show that from subjective evaluation and quantitative analysis the new algorithm overcomes the shortcomings of noise amplification and edge blur when the conventional enhancement algorithm enhances the image. It can improve image darkening and avoid image distortion in JPEG, BMP, GIF, PNG, PSD, and TIFF formats. By comparing with other image enhancement algorithms, the improved algorithm performs better than DCP, SSR, MSR, MSRCR, and CLAHE algorithm in PSNR, SSIM, and IE evaluation indexes. It has a good effect on preserving the edge information and has good adaptability and stability for heavily polluted haze image enhancement.

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…”

Section: New Image Defogging Algorithmmentioning

confidence: 99%

Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

Sun

Wang

et al. 2022

Journal of Environmental and Public Health

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“…Indeed, in Otsu's method, minimizing the value of class-like variance is like maximizing the class-invariance [11], i.e.…”

Section: Image Thresholding Based On Otsu's Methodsmentioning

confidence: 99%

“…For instance, the Otsu's method [11] and Kapur's method [12] which categorize the image into two parts and binarize them based on a threshold point.…”

Section: Introductionmentioning

confidence: 99%

A robust method for skin cancer diagnosis based on interval analysis

et al. 2020

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Early diagnosis of skin cancer from dermoscopy images significantly reduces the mortality due to this cancer. However, several reasons impact the system diagnosis precision. One of the important problems in this process happens during image acquisition. Often, in medical photography, there are some uncertainties like noises and brightness variations, initial digitalization and sampling which affect the image quality. This study presents a new approach for border detection of the cancer area by considering the uncertainties. Interval analysis is utilized to extend the proposed edge detection method and the Hukuhara method is utilized for developing the differentiation formula for edge detection in the interval space. Simulation results are applied to two different skin cancer atlas and the results are compared with three popular methods by considering two types of noises including Gaussian noise and salt-and-pepper noise. The results showed that the introduced method gives better results than the compared methods.

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“…• Color Feature: is one of the most commonly used features for image retrieval and the extensively used visual features in CBIR. It plays a key role in the determination of the visual feature of an image; it is invariant to the complexity and very much sensitive to humans than the greyscale images.This kind of features is independent of image size and orientation and is generally extracted using the Color Histogram techniques, Color coherence vector … etc [27].…”

Section: A Feature Extractionmentioning

confidence: 99%

Withdrawn

2020

IJEAT

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Today, skin cancer is a serious health problem and the quest for a correct clinical diagnosis has been an ongoing concern for dermatologists and for many researchers an important field study. Field Programmable Gate Array (FPGA) technology has become a key part of the current movement towards innovation. It provides a viable target for applying image processing algorithms. It presents a viable target for the implementation of algorithms suited to image processing even under real-time requirements, because of their flexibility and great potential. FPGAs have shown very high performance in spite of their low operational frequency and high parallelism in applications. In this paper, an embedded Content-Based Image Retrieval (CBIR) implementation based on FPGA is proposed to manage the image data efficiently and fastly. We describe the implementation of CBIR in image processing applications using FPGA. The main aim behind this implementation is to reach minimum timing by the maximum utilization of resources. We are conducting a study on the advantages of this embedded approach and discussed its effectiveness for a set of benchmark images publically available dataset PH2. The FPGA-based implementation shows the feasibility of the proposed methodology. The functional implementation of all processes using the Verilog code of FPGA has been compiled on the ALTERA QUARTUS-II software tool.

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A hardware Implementation of OTSU Thresholding Method for Skin Cancer Image Segmentation

Cited by 12 publications

References 9 publications

Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

A robust method for skin cancer diagnosis based on interval analysis

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