Percorrer por autor "Pereira, Pedro M. M."
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- Dermoscopic skin lesion image segmentation based on Local Binary Pattern Clustering: Comparative studyPublication . Pereira, Pedro M. M.; Fonseca-Pinto, Rui; Paiva, Rui Pedro; Assuncao, Pedro A. A.; Tavora, Luis M. N.; Thomaz, Lucas A.; Faria, Sergio M. M.Accurate skin lesion segmentation is important for identification and classification through computational methods. However, when performed by dermatologists, the results of clinical segmentation are affected by a certain margin of inaccuracy (which exists since dermatologist do not delineate lesions for segmentation but for extraction) and also significant inter- and intra-individual variability, such segmentation is not sufficiently accurate for segmentation studies. This work addresses these limitations to enable detailed analysis of lesions’ geometry along with extraction of non-linear characteristics of region-of-interest border lines. A comprehensive review of 39 segmentation methods is carried out and a contribution to improve dermoscopic image segmentation is presented to determine the regions-of-interest of skin lesions, through accurate border lines with fine geometric details. This approach resorts to Local Binary Patterns and k-means clustering for precise identification of lesions boundaries, particularly the melanocytic. A comparative evaluation study is carried out using three different datasets and reviewed algorithms are grouped according to their approach. Results show that algorithms from the same group tend to perform similarly. Nevertheless, their performance does not depend uniquely on the algorithm itself but also on the underlying dataset characteristics. Throughout several evaluations, the proposed Local Binary Patterns method presents, consistently, better average performance than the current state-of-the-art techniques across the three different datasets without the need of training or supervised learning steps. Overall, apart from presenting a new segmentation method capable of outperforming the current state-of-the-art, this paper provides insightful information about the behaviour and performance of different image segmentation algorithms.
- Optimized fast Walsh-Hadamard transform on OpenCL-GPU and OpenCL-CPUPublication . Pereira, Pedro M. M.; Domingues, Patrício; M. M. Rodrigues, Nuno; Faria, Sergio M. M. de; Falcao, GabrielThe Walsh-Hadamard transform plays a major role in many image and video coding algorithms. In one hand, its intensive use in these algorithms makes its acceleration a challenge, in order to speed-up the algorithm execution. On the other hand, the available fast implementations are not efficient across different platforms. In this work, a parallel-based implementation of the WHT is proposed for CPU and GPU platforms using the OpenCL standard. OpenCL achieves portability at code level, but its performance suffers when the same code is used for CPUs and GPUs. To achieve top performance, we propose two WHT versions: OpenCL-GPU for GPUs and OpenCL-CPU for CPUs. Broadly, OpenCL-GPU executed on a GPU runs faster than OpenCL-CPU executed on a multicore CPU, with speedups that range from 120.87 to 1016.35. However, OpenCL-GPU performance drops substantially when ran on a multicore CPU machine, where OpenCL-CPU achieves higher performance, as it exploits the OpenCL support for SIMD instructions.
- Optimizing GPU Code for CPU Execution Using OpenCL and Vectorization: A Case Study on Image CodingPublication . Pereira, Pedro M. M.; Domingues, Patrício; M. M. Rodrigues, Nuno; Gabriel Falcao; Faria, Sergio M. M. deAlthough OpenCL aims to achieve portability at the code level, di erent hardware platforms requires di erent approaches in order to extract the best performance for OpenCL-based code. In this work, we use an image encoder originally tuned for OpenCL on GPU (OpenCL-GPU), and optimize it for multi-CPU based platforms. We produce two OpenCL-based versions: i) a regular one (OpenCL-CPU) and ii) a CPU vector-based one (OpenCL-CPU-Vect). The use of CPU vectorization exploits the OpenCL support, making it much simpler than directly coding with SIMD instructions such as SSE and AVX. Globally, while the OpenCL-GPU version is the fastest when run on a high end GPU requiring around 580 seconds to encode the Lenna image, its performance drops roughly 65% when run unchanged on a multicore CPU machine. For the CPU tuned versions, OpenCL-CPU encodes the Lenna image in 805 seconds, while the vectorization-based approach executes the same operation in 672 seconds. Results show that meaningful performance gains can be achieved by tailoring the OpenCL code to the CPU, and that the use of CPU vectorization instructions through OpenCL is both rather simple and performance rewarding.
- Skin lesion classification enhancement using border-line features – The melanoma vs nevus problemPublication . Pereira, Pedro M. M.; Fonseca-Pinto, Rui; Paiva, Rui Pedro; Assuncao, Pedro A. A.; Tavora, Luis M. N.; Thomaz, Lucas A.; Faria, Sergio M. M.Machine learning algorithms are progressively assuming an important role as a computational tool to support clinical diagnosis, namely in the classification of pigmented skin lesions. The current classification methods commonly rely on features derived from shape, colour, or texture, obtained after image segmentation, but these do not always guarantee the best results. To improve the classification accuracy, this work proposes to further exploit the border-line characteristics of the lesion segmentation mask, by combining gradients with local binary patterns (LBP). In the proposed method, these border-line features are used together with the conventional ones to enhance the performance of skin lesion classification algorithms. When the new features are combined with the classical ones, the experimental results show higher accuracy, which impacts positively the overall performance of the classification algorithms. As the medical image datasets usually present large class imbalance, which results in low sensitivity for the classifiers, the border-line features have a positive impact on this classification metric, as evidenced by the experimental results. Both the features’ usefulness and their impact are assessed in regard to the classification results, which in turn are statistically tested for completeness, using three different classifiers and two medical image datasets.