Ms. Ping Hu Profile

Ping Hu

at Guilin Univ of Technology

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Publications (3)

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Proceedings Article | 23 October 2018 Paper

Pigmented skin lesion segmentation based on random forest and full convolutional neural networks

Tiejun Yang, Shan Peng, Ping Hu, Lin Huang

Proceedings Volume 10820, 108203M (2018) https://doi.org/10.1117/12.2503941

KEYWORDS: Image segmentation, Skin, Convolutional neural networks, Image fusion, Melanoma, Image processing algorithms and systems, Convolution, Image processing, Image classification

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Segmentation of pigmented lesions is often affected by factors such as hair around the skin lesions, artificial markings, etc., and the complexity of the lesion itself, such as lesions and skin boundaries is not clear, the internal color of lesions is variable, etc., resulting in segmentation difficulties. Aiming at the problem that the segmentation method of pigmented skin lesions using only random forests is not accurate, a segmentation method for pigmented skin lesion using a combination of random forest and fully convolutional neural networks (FCN) is proposed. This method firstly classifies and recognizes skin lesion images based on random forests to obtain a probability distribution of the lesions and the background. Then, the other probability distribution is obtained using FCN based on an improved loss function. Finally, the classification results of random forest and FCN are fused into the final image segmentation results. The experimental results show that the combination of random forest and FCN yields better performances than using random forest alone, in particular, can increase the sensitivity by about 20%.

Proceedings Article | 31 October 2016 Paper

Pigmented skin lesion detection using random forest and wavelet-based texture

Ping Hu, Tie-jun Yang

Proceedings Volume 10024, 100241X (2016) https://doi.org/10.1117/12.2245149

KEYWORDS: Skin, Wavelets, Melanoma, Visualization, Image segmentation, Image classification, Image processing algorithms and systems, Image enhancement, Error analysis, Image filtering

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The incidence of cutaneous malignant melanoma, a disease of worldwide distribution and is the deadliest form of skin cancer, has been rapidly increasing over the last few decades. Because advanced cutaneous melanoma is still incurable, early detection is an important step toward a reduction in mortality. Dermoscopy photographs are commonly used in melanoma diagnosis and can capture detailed features of a lesion. A great variability exists in the visual appearance of pigmented skin lesions. Therefore, in order to minimize the diagnostic errors that result from the difficulty and subjectivity of visual interpretation, an automatic detection approach is required. The objectives of this paper were to propose a hybrid method using random forest and Gabor wavelet transformation to accurately differentiate which part belong to lesion area and the other is not in a dermoscopy photographs and analyze segmentation accuracy. A random forest classifier consisting of a set of decision trees was used for classification. Gabor wavelets transformation are the mathematical model of visual cortical cells of mammalian brain and an image can be decomposed into multiple scales and multiple orientations by using it. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain. Texture features based on Gabor wavelets transformation are found by the Gabor filtered image. Experiment results indicate the following: (1) the proposed algorithm based on random forest outperformed the-state-of-the-art in pigmented skin lesions detection (2) and the inclusion of Gabor wavelet transformation based texture features improved segmentation accuracy significantly.

Proceedings Article | 31 October 2016 Paper

Pedestrian detection based on redundant wavelet transform

Lin Huang, Liping Ji, Ping Hu, Tiejun Yang

Proceedings Volume 10020, 1002017 (2016) https://doi.org/10.1117/12.2246044

KEYWORDS: Motion estimation, Wavelet transforms, Video surveillance, Image analysis, Image segmentation, Video, Cameras, Feature extraction, Databases, Computer programming

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Intelligent video surveillance is to analysis video or image sequences captured by a fixed or mobile surveillance camera, including moving object detection, segmentation and recognition. By using it, we can be notified immediately in an abnormal situation. Pedestrian detection plays an important role in an intelligent video surveillance system, and it is also a key technology in the field of intelligent vehicle. So pedestrian detection has very vital significance in traffic management optimization, security early warn and abnormal behavior detection. Generally, pedestrian detection can be summarized as: first to estimate moving areas; then to extract features of region of interest; finally to classify using a classifier. Redundant wavelet transform (RWT) overcomes the deficiency of shift variant of discrete wavelet transform, and it has better performance in motion estimation when compared to discrete wavelet transform. Addressing the problem of the detection of multi-pedestrian with different speed, we present an algorithm of pedestrian detection based on motion estimation using RWT, combining histogram of oriented gradients (HOG) and support vector machine (SVM). Firstly, three intensities of movement (IoM) are estimated using RWT and the corresponding areas are segmented. According to the different IoM, a region proposal (RP) is generated. Then, the features of a RP is extracted using HOG. Finally, the features are fed into a SVM trained by pedestrian databases and the final detection results are gained. Experiments show that the proposed algorithm can detect pedestrians accurately and efficiently.

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