Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, ...is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately.
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•Automatic selection of the Localized Region-based Active Contour Model (LRACM).•Statistical moment-based features as image descriptors.•Automatic Brain tumor segmentation framework.•LRACM performance depends on the image content.•Fast and reliable MRI data analysis.
Autonomous robot visual navigation is a fundamental locomotion task based on extracting relevant features from images taken from the surrounded environment to control an independent displacement. In ...the navigation, the use of a known visual map helps obtain an accurate localization, but in the absence of this map, a guided or free exploration pathway must be executed to obtain the images sequence representing the visual map. This paper presents an appearance-based localization method based on a visual map and an end-to-end Convolutional Neural Network (CNN). The CNN is initialized via transfer learning (trained using the ImageNet dataset), evaluating four state-of-the-art CNN architectures: VGG16, ResNet50, InceptionV3, and Xception. A typical pipeline for transfer learning includes changing the last layer to adapt the number of neurons according to the number of custom classes. In this work, the dense layers after the convolutional and pooling layers were substituted by a Global Average Pooling (GAP) layer, which is parameter-free. Additionally, an L2-norm constraint was added to the GAP layer feature descriptors, restricting the features from lying on a fixed radius hypersphere. These different pre-trained configurations were analyzed and compared using two visual maps found in the CIMAT-NAO datasets consisting of 187 and 94 images, respectively. For evaluating the localization tasks, a set of 278 and 94 images were available for each visual map, respectively. The numerical results proved that by integrating the L2-norm constraint in the training pipeline, the appearance-based localization performance is boosted. Specifically, the pre-trained VGG16 and Xception networks achieved the best localization results, reaching a top-3 accuracy of 90.70% and 93.62% for each dataset, respectively, overcoming the referenced approaches based on hand-crafted feature extractors.
Computational cell segmentation is a vital area of research, particularly in the analysis of images of cancer cells. The use of cell lines, such as the widely utilized HeLa cell line, is crucial for ...studying cancer. While deep learning algorithms have been commonly employed for cell segmentation, their resource and data requirements can be impractical for many laboratories. In contrast, image processing algorithms provide a promising alternative due to their effectiveness and minimal resource demands. This article presents the development of an algorithm utilizing digital image processing to segment the nucleus and shape of HeLa cells. The research aims to segment the cell shape in the image center and accurately identify the nucleus. The study uses and processes 300 images obtained from Serial Block-Face Scanning Electron Microscopy (SBF-SEM). For cell segmentation, the morphological operation of erosion was used to separate the cells, and through distance calculation, the cell located at the center of the image was selected. Subsequently, the eroded shape was employed to restore the original cell shape. The nucleus segmentation uses parameters such as distances and sizes, along with the implementation of verification stages to ensure accurate detection. The accuracy of the algorithm is demonstrated by comparing it with another algorithm meeting the same conditions, using four segmentation similarity metrics. The evaluation results rank the proposed algorithm as the superior choice, highlighting significant outcomes. The algorithm developed represents a crucial initial step towards more accurate disease analysis. In addition, it enables the measurement of shapes and the identification of morphological alterations, damages, and changes in organelles within the cell, which can be vital for diagnostic purposes.
•Computational cell detection is benefits, provides the understanding of disease diagnosis and cellular behavior.•Computer vision techniques are an excellent alternative for object detection in images.•The complexity of cell shapes in images directly affects effective computational detection.•It is possible to verify a nucleus through distance measurement and shape validation within the cell.•Our algorithm detects nuclei within cells, even if they are ill-defined or located in the center of the image.
In this paper, we introduce an image enhancing approach for transforming dark images into lightened scenes, and we evaluate such method in different perceptual color spaces, in order to find the ...best-suited for this particular task. Specifically, we use a classical color transfer method where we obtain first-order statistics from a target image and transfer them to a dark input, modifying its hue and brightness. Two aspects are particular to this paper, the application of color transfer on dark imagery and in the search for the best color space for the application. In this regard, the tests performed show an accurate transference of colors when using perceptual color spaces, being RLAB the best color space for the procedure. Our results show that the methodology presented in this paper can be a good alternative to low-light or night vision processing techniques. Besides, the proposed method has a low computational complexity, property that is important for real time applications or for low-resource systems. This method can be used as a preprocessing step in order to improve the recognition and interpretation of dark imagery in a wide range of applications.
The quality assurance of fabrics is a fundamental issue in the textile manufacturing industry. Automatic and accurate detection of defects is one of the most important and challenging tasks in order ...to guarantee the quality of fabrics. In this paper, we propose an approach for the defect detection on textiles with patterned texture using a rule-based classification system and the local binary features. In our proposal, rules are automatically learned from the textile samples using a rough-set-based approach. The proposed system analyzes the texture of fabrics using a combination of local binary features, which have shown to be highly discriminatory. Our approach is performed in two stages: training and testing. During the training stage, binary features from both defective and defect-free images are extracted and used to formulate an ensemble of the rough-set-based rules. For the testing stage, we submit different samples of fabrics, and they are classified as defective or defect-free. The proposed method is quantitatively evaluated on an extensive dataset of images of the defective fabrics. These experiments show that the proposed approach results in higher accuracy, in comparison with those obtained by the state-of-the-art methods.
This paper represents a twofold approach. On the one hand, we introduce a simple method that encompasses an ensemble of image processing algorithms with a multilevel color transfer approach at its ...core. On the other hand, the method is applied for providing an artistic look to standard images. The approach proposes a multilevel color transfer in a chromatic channel of the CIELAB color space. Once converted from red, green and blue, a specific channel on both images, input and target (reference), is thresholded in a number of levels. Later, the color transfer is performed between regions from corresponding levels using a classical color transfer method. In the application phase, the color palette of a recognized artwork of the Fauve movement is mapped to the input image, emulating the sight of the artist, characterized by the use of vivid colors. Filtering techniques are applied to the outcome, in order to emulate the basic brushstrokes of the artist. Experimental results are shown, visualizing and comparing the input images with the outcomes.
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•A fuzzy rule-based system operates as a selector of color constancy algorithms.•The system selects among the White-Patch, Gray-World and Gray-Edge algorithms.•The method attains a ...high rate of correct selection according to the actual scene.•Two problems are addressed simultaneously: color constancy and color enhancement.•The framework can be used in engineering applications, like video surveillance.
This work introduces a fuzzy rule-based system operating as a selector of color constancy algorithms for the enhancement of dark images. In accordance with the actual content of an image, the system selects among three color constancy algorithms, the White-Patch, the Gray-World and the Gray-Edge. These algorithms have been considered because of their accurate remotion of the illuminant, besides showing an outstanding color enhancement on images. The design of the rule-based system is not a trivial task because several features are involved in the selection. Our proposal consists in a fuzzy system, modeling the decision process through simple rules. This approach can handle large amounts of information and is tolerant to ambiguity, while addressing the problem of dark image enhancement. The methodology consists in two main stages. Firstly, a training protocol determines the fuzzy rules, according to features computed from a subset of training images taken from the SFU Laboratory dataset. We choose carefully twelve image features for the formulation of the rules: seven color features, three texture descriptors, and two lighting-content descriptors. In the rules, the fuzzy sets are modeled using Gaussian membership functions. Secondly, experiments are carried out using Mamdani and Larsen fuzzy inferences. For a test image, a color constancy algorithm is selected according to the inference process and the rules previously defined. The results show that our method attains a high rate of correct selection of the most well-suited algorithm for the particular scene.
The consumption of edible fungi has increased worldwide due to its nutritional and functional properties. In the state of Guanajuato, Mexico, the production of these fungi depends on the import of ...strains from different species. This is due to a lack of isolation and characterization of regional mycogenetic resources such as in the case of the “Maguey mushroom”. The aim of this study was to carry out the in vitro molecular identification, morphological characterization, and antioxidant biochemistry of a strain of Pleurotus sp. in Guanajuato, Mexico, and to compare it to commercial strains. The hypothesis proposed is based on the possibility of isolating a regional strain with adequate mycelial characteristics for its in vitro cultivation. A wild strain called UG-01 was collected and isolated from an Agave mapisaga plant and its identity was determined by sequencing the region ITS1-5.8S-ITS2. The strain was cultivated in vitro, and the mycelium was characterized; an experimental strain and four commercial ones were included as a control. The results were analysed in a totally randomized design. The “Maguey mushroom” strain was identified as Pleurotus djamor. Based on the CIE L*a*b system, the coordinates 86.2, -5.6 and -4.1 were obtained, along with 33.1 and 21.7 for Hue and Chroma. The UG-01 strain displayed the highest growth rate with a cottonlike structure and without exudates. In addition, it presented the highest concentration of proline, phenolic compounds, and flavonoids, as well as the lowest remains of 2,2-diphenyl-1-picrylhydrazyl (DPPH). In conclusion, it was possible to cultivate the mycelium of the “Maguey mushroom” in vitro, which displayed better morphological and antioxidant biochemistry in comparison with the imported commercial strains.
Color constancy is an important process in a number of vision tasks. Most devices for capturing images operate on the RGB color space and, usually, the processing of the images is in this space, ...although some processes have shown a better performance when a perceptual color space is used instead. In this paper, experiments on the White Patch Retinex, a color constancy algorithm commonly used, are performed in two color spaces, RGB and CIELAB, for comparison purposes. Experimental results using an imagery set are analyzed using a no-reference quality metric and outcomes are discussed. It has been found that the White Patch Retinex algorithm shows a better performance in RGB than in CIELAB, but when color adjustments are implemented in sequence, firstly in CIELAB and then in RGB, much better results are obtained.
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