Real-time classification based on color and texture attributes on an FPGA-based architecture

Ibarra-Manzano, Mario Alberto; Devy, Michel; Boizard, Jean-Louis

doi:10.1109/dasip.2010.5706272

Cited by 9 publications

(2 citation statements)

References 9 publications

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“…Furthermore, it stores relevant information about the image content. In this work, we adopt the SDH method used in [44,45] for calculating texture features from signals. As we mentioned above, SDH computes histograms that collect results of the addition and subtraction of images.…”

Section: Methodsmentioning

confidence: 99%

Contrast and Homogeneity Feature Analysis for Classifying Tremor Levels in Parkinson’s Disease Patients

Vivar

Almanza-Ojeda

Cheng

et al. 2019

Sensors

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Early detection of different levels of tremors helps to obtain a more accurate diagnosis of Parkinson’s disease and to increase the therapy options for a better quality of life for patients. This work proposes a non-invasive strategy to measure the severity of tremors with the aim of diagnosing one of the first three levels of Parkinson’s disease by the Unified Parkinson’s Disease Rating Scale (UPDRS). A tremor being an involuntary motion that mainly appears in the hands; the dataset is acquired using a leap motion controller that measures 3D coordinates of each finger and the palmar region. Texture features are computed using sum and difference of histograms (SDH) to characterize the dataset, varying the window size; however, only the most fundamental elements are used in the classification stage. A machine learning classifier provides the final classification results of the tremor level. The effectiveness of our approach is obtained by a set of performance metrics, which are also used to show a comparison between different proposed designs.

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Section: Methodsmentioning

confidence: 99%

Contrast and Homogeneity Feature Analysis for Classifying Tremor Levels in Parkinson’s Disease Patients

Vivar

Almanza-Ojeda

Cheng

et al. 2019

Sensors

Self Cite

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“…An overview of our proposed approach is depicted in Figure 1, presenting mainly three stages: (1) feature extraction, (2) analysis of calcifications, and (3) classification of the mammography. First, the cp texture feature is computed from a mammogram using the sum and difference histograms (SDH) technique [21].…”

Section: Methodsmentioning

confidence: 99%

Analysis of the Cluster Prominence Feature for Detecting Calcifications in Mammograms

Cruz-Bernal

Flores-Barranco

Almanza-Ojeda

et al. 2018

Journal of Healthcare Engineering

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In mammograms, a calcification is represented as small but brilliant white region of the digital image. Earlier detection of malignant calcifications in patients provides high expectation of surviving to this disease. Nevertheless, white regions are difficult to see by visual inspection because a mammogram is a gray-scale image of the breast. To help radiologists in detecting abnormal calcification, computer-inspection methods of mammograms have been proposed; however, it remains an open important issue. In this context, we propose a strategy for detecting calcifications in mammograms based on the analysis of the cluster prominence (cp) feature histogram. The highest frequencies of the cp histogram describe the calcifications on the mammography. Therefore, we obtain a function that models the behaviour of the cp histogram using the Vandermonde interpolation twice. The first interpolation yields a global representation, and the second models the highest frequencies of the histogram. A weak classifier is used for obtaining a final classification of the mammography, that is, with or without calcifications. Experimental results are compared with real DICOM images and their corresponding diagnosis provided by expert radiologists, showing that the cp feature is highly discriminative.

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Tremor Signal Analysis for Parkinson’s Disease Detection Using Leap Motion Device

Vivar-Estudillo

Ibarra-Manzano

Almanza-Ojeda

2018

Advances in Soft Computing

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Real-time classification based on color and texture attributes on an FPGA-based architecture

Cited by 9 publications

References 9 publications

Contrast and Homogeneity Feature Analysis for Classifying Tremor Levels in Parkinson’s Disease Patients

Contrast and Homogeneity Feature Analysis for Classifying Tremor Levels in Parkinson’s Disease Patients

Analysis of the Cluster Prominence Feature for Detecting Calcifications in Mammograms

Tremor Signal Analysis for Parkinson’s Disease Detection Using Leap Motion Device

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