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Xavier Baro, Sergio Escalera, Jordi Vitria, Oriol Pujol, & Petia Radeva. (2009). Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification. TITS - IEEE Transactions on Intelligent Transportation Systems, 10(1), 113–126.
Abstract: The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we introduce a novel approach for the detection and classification of traffic signs. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost, which allows the use of large feature spaces. Classification is defined as a multiclass categorization problem. A battery of classifiers is trained to split classes in an Error-Correcting Output Code (ECOC) framework. We propose an ECOC design through a forest of optimal tree structures that are embedded in the ECOC matrix. The novel system offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination.
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Md. Mostafa Kamal Sarker, Hatem A. Rashwan, Farhan Akram, Vivek Kumar Singh, Syeda Furruka Banu, Forhad U H Chowdhury, et al. (2021). SLSNet: Skin lesion segmentation using a lightweight generative adversarial network. ESWA - Expert Systems With Applications, 183, 115433.
Abstract: The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the color, texture and shapes of skin lesions. Existing deep learning-based skin lesion segmentation algorithms are expensive in terms of computational time and memory. Consequently, running such segmentation algorithms requires a powerful GPU and high bandwidth memory, which are not available in dermoscopy devices. Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model. The 1-D kernel factorized network reduces the computational cost of 2D filtering. The position and channel attention modules enhance the discriminative ability between the lesion and non-lesion feature representations in spatial and channel dimensions, respectively. A multiscale block is also used to aggregate the coarse-to-fine features of input skin images and reduce the effect of the artifacts. SLSNet is evaluated on two publicly available datasets: ISBI 2017 and the ISIC 2018. Although SLSNet has only 2.35 million parameters, the experimental results demonstrate that it achieves segmentation results on a par with the state-of-the-art skin lesion segmentation methods with an accuracy of 97.61%, and Dice and Jaccard similarity coefficients of 90.63% and 81.98%, respectively. SLSNet can run at more than 110 frames per second (FPS) in a single GTX1080Ti GPU, which is faster than well-known deep learning-based image segmentation models, such as FCN. Therefore, SLSNet can be used for practical dermoscopic applications.
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Simone Balocco, Carlo Gatta, Oriol Pujol, J. Mauri, & Petia Radeva. (2010). SRBF: Speckle Reducing Bilateral Filtering. UMB - Ultrasound in Medicine and Biology, 36(8), 1353–1363.
Abstract: Speckle noise negatively affects medical ultrasound image shape interpretation and boundary detection. Speckle removal filters are widely used to selectively remove speckle noise without destroying important image features to enhance object boundaries. In this article, a fully automatic bilateral filter tailored to ultrasound images is proposed. The edge preservation property is obtained by embedding noise statistics in the filter framework. Consequently, the filter is able to tackle the multiplicative behavior modulating the smoothing strength with respect to local statistics. The in silico experiments clearly showed that the speckle reducing bilateral filter (SRBF) has superior performances to most of the state of the art filtering methods. The filter is tested on 50 in vivo US images and its influence on a segmentation task is quantified. The results using SRBF filtered data sets show a superior performance to using oriented anisotropic diffusion filtered images. This improvement is due to the adaptive support of SRBF and the embedded noise statistics, yielding a more homogeneous smoothing. SRBF results in a fully automatic, fast and flexible algorithm potentially suitable in wide ranges of speckle noise sizes, for different medical applications (IVUS, B-mode, 3-D matrix array US).
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Antonio Hernandez, Carlo Gatta, Sergio Escalera, Laura Igual, Victoria Martin-Yuste, Manel Sabate, et al. (2012). Accurate coronary centerline extraction, caliber estimation and catheter detection in angiographies. TITB - IEEE Transactions on Information Technology in Biomedicine, 16(6), 1332–1340.
Abstract: Segmentation of coronary arteries in X-Ray angiography is a fundamental tool to evaluate arterial diseases and choose proper coronary treatment. The accurate segmentation of coronary arteries has become an important topic for the registration of different modalities which allows physicians rapid access to different medical imaging information from Computed Tomography (CT) scans or Magnetic Resonance Imaging (MRI). In this paper, we propose an accurate fully automatic algorithm based on Graph-cuts for vessel centerline extraction, caliber estimation, and catheter detection. Vesselness, geodesic paths, and a new multi-scale edgeness map are combined to customize the Graph-cuts approach to the segmentation of tubular structures, by means of a global optimization of the Graph-cuts energy function. Moreover, a novel supervised learning methodology that integrates local and contextual information is proposed for automatic catheter detection. We evaluate the method performance on three datasets coming from different imaging systems. The method performs as good as the expert observer w.r.t. centerline detection and caliber estimation. Moreover, the method discriminates between arteries and catheter with an accuracy of 96.5%, sensitivity of 72%, and precision of 97.4%.
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Mohammad Momeny, Ali Asghar Neshat, Ahmad Jahanbakhshi, Majid Mahmoudi, Yiannis Ampatzidis, & Petia Radeva. (2023). Grading and fraud detection of saffron via learning-to-augment incorporated Inception-v4 CNN. FC - Food Control, 147, 109554.
Abstract: Saffron is a well-known product in the food industry. It is one of the spices that are sometimes adulterated with the sole motive of gaining more economic profit. Today, machine vision systems are widely used in controlling the quality of food and agricultural products as a new, non-destructive, and inexpensive approach. In this study, a machine vision system based on deep learning was used to detect fraud and saffron quality. A dataset of 1869 images was created and categorized in 6 classes including: dried saffron stigma using a dryer; dried saffron stigma using pressing method; pure stem of saffron; sunflower; saffron stem mixed with food coloring; and corn silk mixed with food coloring. A Learning-to-Augment incorporated Inception-v4 Convolutional Neural Network (LAII-v4 CNN) was developed for grading and fraud detection of saffron in images captured by smartphones. The best policies of data augmentation were selected with the proposed LAII-v4 CNN using images corrupted by Gaussian, speckle, and impulse noise to address overfitting the model. The proposed LAII-v4 CNN compared with regular CNN-based methods and traditional classifiers. Ensemble of Bagged Decision Trees, Ensemble of Boosted Decision Trees, k-Nearest Neighbor, Random Under-sampling Boosted Trees, and Support Vector Machine were used for classification of the features extracted by Histograms of Oriented Gradients and Local Binary Patterns, and selected by the Principal Component Analysis. The results showed that the proposed LAII-v4 CNN with an accuracy of 99.5% has achieved the best performance by employing batch normalization, Dropout, and leaky ReLU.
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