A New Pseudo-color Transform for Fibre Masses Inspection of Industrial Images

Lu, Xiang-Ju; Ding, Mingxiao; Wang, Yunkuan

doi:10.1016/s1874-1029(08)60075-0

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…These four criteria are made as input to the neural network (which is the smart diagnosis in the second part) to classify cervical cells to three stages namely normal, Low-grade Squamous Intraepithelial Lesions, (LSIL) and High-grade Squamous Intraepithelial Lesions (HSIL). Nevertheless, the NeuralPap system processes image at a grey scale, despite image processing through pseudocolouring becoming increasingly popular in medical imaging applications [18][19][20][21][22][23]. This is owing to the fact that the human visual system is more sensitive to colours from monochromatic images.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Features Extraction Process and Classification of Cervical Cancer for the NeuralPap System

Sulaiman¹,

Isa²,

Othman³

et al. 2015

Procedia Computer Science

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Cervical cancer has caused many deaths each year. Screening tests, such as Pap smear test used for the detection of the precancerous stage are able to avoid the occurrence of cervical cancer. However, the Pap smear test has several disadvantages such as less effective slides preparation and human error. Therefore, a computer-aided diagnosis system is introduced as a solution to the problem. One of the diagnostic systems that has been built is NeuralPap. However, the NeuralPap performance is limited by several constraints. This research proposed several new image processing algorithms to reduce these constraints. The Adaptive Fuzzy-k-Means (AFKM) clustering algorithm is proposed to replace the Moving k-Means (MKM) to segment Pap smear images into the nucleus, cytoplasm and background regions. Next, the feature extraction algorithm based on pseudo colouring called the Pseudo Colour Feature Extraction (PCFE) manual and Semi-Automatic PCFE are designed to replace the Region Growing Based Feature Extraction (RGBFE) which uses monochromatic images. This research is a step forward compared with the NeuralPap system by proposing the feature extraction algorithm for overlapping cells by combining the concept of colour space with Semi-Automatic PCFE algorithm. In addition, this research has also suggested the AFKM algorithm as a new centre positioning algorithm for the Radial Basis Function (RBF) and Hybrid RBF (HRBF) networks replacing the MKM algorithm. The entire proposed algorithm has been proven to produce better performance than the corresponding algorithm used in the NeuralPap. In addition, the combination of all algorithms has managed to increase the accuracy of the classification of cervical cancer to 76.35%, compared with 73.40% which is obtained from the previous NeuralPap system.

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Section: Introductionmentioning

confidence: 99%

Improvement of Features Extraction Process and Classification of Cervical Cancer for the NeuralPap System

Sulaiman¹,

Isa²,

Othman³

et al. 2015

Procedia Computer Science

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“…In principle, color imagery has several benefits over monochrome imagery for surveillance, reconnaissance, and security applications. Since people can discriminate several thousands of colors defined by varying hue, saturation, and brightness, a false-color representation may facilitate night vision image recognition and interpretation [6]- [7]. For instance, color may improve feature contrast, which allows for better scene segmentation and object detection [6] and perfect visual perception under the lighting conditions [8] [9].…”

Section: Introductionmentioning

confidence: 99%

“…Since people can discriminate several thousands of colors defined by varying hue, saturation, and brightness, a false-color representation may facilitate night vision image recognition and interpretation [6]- [7]. For instance, color may improve feature contrast, which allows for better scene segmentation and object detection [6] and perfect visual perception under the lighting conditions [8] [9]. Simply producing a false-color night vision image by mapping multiple images into a three dimensional color space (i.e., YUV space, where Y denotes luminance, U stands for difference between blue and luminance, and V stands for difference between red and luminance. )…”

Section: Introductionmentioning

confidence: 99%

Brightness and Color Transfer for Infrared Images in Vehicle Night Vision System

Bao

2012

AMM

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The automotive night vision system can significantly improve the safety of driving at night by fusion of visible and infrared images. Due to the aim at helping driving at night, it’s important to develop the visibility and the consistency of the image and the real condition on the road. In this paper, visible light image is enhanced its light and shadow information based on nonlinear exponential function and the infrared image is increased its contrast by S-Retriex method. Then the enhancement images are fused and the color space of fusion image is adjusted by the reference statistical characteristics. Simulations have shown that the result image has good visual scene which retains the details of the infrared image and has original light, shadow and color information.

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“…In another report Haldankar and colleagues [4 ] provide the benefits of a color image as a sample image, create a mapping to color images of black and white image, and then they use the appropriate gray value mapping to color black and white image, the results of the technology further improvement of than [1]. In another technology [5] process and colleagues said of image histograms operation and define a overlapping choose different light intensity in different levels of color image, is the basic idea, although this technology is not eligible for good quality of the image detail and in some cases the image color contrast developed but still is desirable, the image can be not a lot of details, in [6 ], colleagues industrial image coloring performance, of course, image into smaller pieces so that the results obtained can move better but still restrict the use of images, there were a lot of details. Method from [7] by John and his colleagues used a color table, the user can choose before the color.…”

Section: Introductionmentioning

confidence: 99%

The Application of Heuristic Method in Images Pseudo Coloring

Liu

Zhao

Zheng

2012

AMM

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In this paper, we use the surface of the analytical sample image recognition color histogram and adaptive modeling, black and white image are doing pseudo shading. Choose a different value in the layer R, G, and B is the most important achievement, of the technology in this method based on the analysis of the image histogram in characteristic value of different colors in the designated, different levels of image, we take action. The implementation results concept for this article to improve compared with other similar methods.

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A New Pseudo-color Transform for Fibre Masses Inspection of Industrial Images

Cited by 5 publications

References 27 publications

Improvement of Features Extraction Process and Classification of Cervical Cancer for the NeuralPap System

Improvement of Features Extraction Process and Classification of Cervical Cancer for the NeuralPap System

Brightness and Color Transfer for Infrared Images in Vehicle Night Vision System

The Application of Heuristic Method in Images Pseudo Coloring

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