A computer-aided diagnosis system for malignant melanomas

Razmjooy, Navid; Mousavi, B. Somayeh; Soleymani, Fazlollah; Khotbesara, Maryam Hosseini

doi:10.1007/s00521-012-1149-1

Cited by 52 publications

(33 citation statements)

References 13 publications

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“…Among the techniques compared in Fig. 7, Razmjooy et al 13 , Wang et al 15 , and Gerald et al 14 were methods designed specifically for lesion segmentation, FCM 4 and the Otsu methods 6 were established as general purpose segmentation techniques that have been widely used in other areas of the application. As for the two rightmost methods on Fig.…”

Section: Discussion and Comparisonmentioning

confidence: 99%

“…The algorithm of this filter replaces each pixel with the median value of the neighbouring pixels. This method is also useful for removing light reflection in images, as well as other small dotted noise in the background outside the skin area 13,16 . Some researchers [17][18][19] have www.scienceasia.org applied the Dullrazor software in order to remove hair.…”

Section: Pre-processingmentioning

confidence: 99%

“…Razmjooy et al 13 proposed a method to convert the colour images with RGB colour space to mode intensity images because the images in their database had varied colour tones and were captured using different devices. In addition, they implemented Kapur's entropy method 13 to select a good value of thresholding. The skin lesion was then segmented from the healthy skin after implementing morphological operations.…”

Section: Introductionmentioning

confidence: 99%

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Automatic skin lesion segmentation with optimal colour channel from dermoscopic images

Al-abayechi¹,

Guo²,

Tan³

et al. 2014

ScienceAsia

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This article provides an improved automated skin lesion segmentation method for dermoscopic images. There are several stages for this method. These include the pre-processing steps such as resizing the images and eliminating noise. Hair was removed and reflective light was reduced using morphological operations and a median filter. The single green channel was rescaled into new intensities, as it provided the highest segmentation accuracy. The threshold value was calculated to separate the skin lesion region from healthy skin. Morphological operations were implemented to merge the small lesion areas around the bigger lesion areas with similar features and trace the boundary of the melanoma. The accuracy of the segmentation was evaluated by comparing the automatic boundary and manual boundary. Compared to other studies, our proposed method achieved the highest average accuracy of 97%.

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Section: Discussion and Comparisonmentioning

confidence: 99%

Section: Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic skin lesion segmentation with optimal colour channel from dermoscopic images

Al-abayechi¹,

Guo²,

Tan³

et al. 2014

ScienceAsia

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“…This reason leads researchers to work more about improving the CAD diagnosis systems. 10 Three main steps for tumor detection based on image processing are preprocessing, image segmentation, image feature extraction, and finally image classification based on the achieved features [11][12][13] (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

Gong

Gao

Abza

2019

Computational Intelligence

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Accurate and early detection of the brain tumor region has a great impact on the choice of treatment, its success rate, and the follow‐up of the disease process over time. This study presents a new bioinspired technique for the early detection of the brain tumor area to improve the chance of completely healing. The study presents a multistep technique to detect the brain tumor area. Herein, after image preprocessing and image feature extraction, an artificial neural network is used to determine the tumor area in the image. The method is based on using an improved version of the whale optimization algorithm for optimal selection of the features and optimizing the artificial neural network weights for classification. Simulation results of the proposed method are applied to FLAIR, T1, and T2 datasets and are compared with different algorithms. Three performance indexes including correct detection rate, false acceptance rate, and false rejection rate are selected for the system performance analysis. Final results showed the superiority of the proposed method toward the other similar methods.

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“…In addition to aforementioned clustering techniques, colour table generalization method based on Support Vector Machines (SVM) classification model was suggested in order to organize the clusters. This is achieved by removing extreme value, filling the holes within the clusters and making the shape and boundaries of a cluster smoother [15][16][17][18]. Budden's work concludes that the optimal method for automatic pixels clustering is the Kmeans algorithm without adopting SVM.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Automatic Colour Calibration for NAO Humanoids

Bolotnikova

Anbarjafari

2018

Teh. vjesn.

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A challenge in NAO soccer robots is colour calibration. Good colour calibration can result in robust and accurate self-localization of the robot. Currently manual calibration is the only solution, which is used. In this paper, we are proposing an automatic real-time, accurate YUV colour space based colour calibration technique. In order to define average values for the desired colour classes namely orange, white, green and purple, a specified set of frames from the NAO camera are analysed. These average values are corrected by luminance analysis of a new frame and are passed to the K-means clustering algorithm as a set of initial means. In addition to these four values, a set of initial means of the K-means algorithm contains 16 values that are calculated in the following manner: the frame being processed is divided into 4 by 4 grids and the average value from every grid serves as an initial mean for K-means clustering. Consequently, colours of a similar type are combined into clusters. The final step of the proposed technique is cluster classification in which the average values of the desired colour classes are corrected by luminance analysis. As NAO cameras provide video streams in YUV format and the proposed algorithm uses this format there is no need for additional computational steps for conversation between the colour spaces. As a result, computational process is reduced compared to current techniques.

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A computer-aided diagnosis system for malignant melanomas

Cited by 52 publications

References 13 publications

Automatic skin lesion segmentation with optimal colour channel from dermoscopic images

Automatic skin lesion segmentation with optimal colour channel from dermoscopic images

Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

Real-Time Automatic Colour Calibration for NAO Humanoids

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