Granulometric analysis based on the energy of Wavelet Transform coefficients

Oliveira, Elisângela Fátima de; Bianchi, Andrea Gomes Campos; Martins-Filho, Luiz S.; Machado, Romuel F.

doi:10.1590/s0370-44672010000200020

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…The energy in these components and their wave coefficients are related to the energy of the original signal. According to Parseval’s theorem, the energy contained in the signal is equal to the sum of the energy contained in the coefficients of detail and approximation in the different resolution levels of the wavelet transform [47,48]. That is, the signal energy can be decomposed in terms of the coefficients of transformation.…”

Section: Mathematical Tool For Processingmentioning

confidence: 99%

A Non-Invasive Medical Device for Parkinson’s Patients with Episodes of Freezing of Gait

Punin

Barzallo

Clotet

et al. 2019

Sensors

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A critical symptom of Parkinson’s disease (PD) is the occurrence of Freezing of Gait (FOG), an episodic disorder that causes frequent falls and consequential injuries in PD patients. There are various auditory, visual, tactile, and other types of stimulation interventions that can be used to induce PD patients to escape FOG episodes. In this article, we describe a low cost wearable system for non-invasive gait monitoring and external delivery of superficial vibratory stimulation to the lower extremities triggered by FOG episodes. The intended purpose is to reduce the duration of the FOG episode, thus allowing prompt resumption of gait to prevent major injuries. The system, based on an Android mobile application, uses a tri-axial accelerometer device for gait data acquisition. Gathered data is processed via a discrete wavelet transform-based algorithm that precisely detects FOG episodes in real time. Detection activates external vibratory stimulation of the legs to reduce FOG time. The integration of detection and stimulation in one low cost device is the chief novel contribution of this work. We present analyses of sensitivity, specificity and effectiveness of the proposed system to validate its usefulness.

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Section: Mathematical Tool For Processingmentioning

confidence: 99%

A Non-Invasive Medical Device for Parkinson’s Patients with Episodes of Freezing of Gait

Punin

Barzallo

Clotet

et al. 2019

Sensors

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“…Decomposition through wavelet transform enables different image frequency levels analyses. High frequency components enhance boundaries and other fine details of image objects, such as the nuclear regions we are interested in [55,56]. Daubechies 5, Symlet 8 and Bi-orthogonal 3.7 wavelet families were analysed and the last one gave the best results, as presented in Section 3.…”

Section: Segmentationmentioning

confidence: 99%

“…6 In this step, an investigation was performed with the proposal to analyse the differences of wavelet details sub-bands of healthy and neoplastic nuclei. Since the approximation sub-band contains only low-frequency components and it corresponds to a low reso- lution of the signal representation, it does not allow the analysis of signal details in the high frequencies [52,56]. So this sub-band was not considered in this analysis.…”

Section: Segmentationmentioning

confidence: 99%

Using wavelet sub-band and fuzzy 2-partition entropy to segment chronic lymphocytic leukemia images

Tosta

Faria

Batista

et al. 2018

Applied Soft Computing

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Histological images analysis is an important procedure to diagnose different types of cancer. One of them is the chronic lymphocytic leukemia (CLL), which can be identified by applying image segmentation techniques. This study presents an unsupervised method to segment neoplastic nuclei in CLL images. Firstly, deconvolution, histogram equalization and mean filter were applied to enhance nuclear regions. Then, a segmentation technique based on a combination of wavelet transform, fuzzy 2-partition entropy and genetic algorithm was used, followed by removal of false positive regions, and application of valley-emphasis and morphological operations. In order to evaluate the proposed algorithm H&E-stained histological images were used. In the accuracy metric, the proposed method attained more than 80%, which can surpass similar methods. This proposal presents spatial distribution that has a good consistency with a manual segmentation and lower overlapping rate than other techniques in the literature.

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“…As a result, the total high frequency corresponding to the detail coefficients is evaluated by (11) (Oliveira et al, 2010) as follows: (11) To normalize the total high frequency energy, F, so that it does not depend on the size of the image Pre-processingTechniquestoImprovetheEfficiencyofVideoIdentificationforthePygmyBluetongueLizard for comparison, it is divided by the size, which is the total number of pixels, , in the lizard, as given by (12). (12) where is the normalized total high frequency energy.…”

Section: High Frequency Energy Measurementmentioning

confidence: 99%

Pre-processing Techniques to Improve the Efficiency of Video Identification for the Pygmy Bluetongue Lizard

Tohl

Bull

2015

Proceedings of the 10th International Conference on Computer Vision Theory and Applications

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Abstract:In the study of the endangered Pygmy Bluetongue Lizard, non-invasive photographic identification is preferred to the current invasive methods which can be unreliable and cruel. As the lizard is an endangered species, there are restrictions on its handling. The lizard is also in constant motion and it is therefore difficult to capture a good still image for identification purposes. Hence video capture is preferred as a number of images of the lizard at various positions and qualities can be collected in just a few seconds from which the best image can be selected for identification. With a large number of individual lizards in the database, matching a video sequence of images against each database image for identification will render the process very computationally inefficient. Moreover, a large portion of those images are non-identifiable due to motion and optical blur and different body curvature to the reference database image. In this paper, we propose a number of pre-processing techniques for pre-selecting the best image out of the video image sequence for identification. Using our proposed pre-selection techniques, it has been shown that the computational efficiency can be significantly improved.

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Granulometric analysis based on the energy of Wavelet Transform coefficients

Cited by 6 publications

References 11 publications

A Non-Invasive Medical Device for Parkinson’s Patients with Episodes of Freezing of Gait

A Non-Invasive Medical Device for Parkinson’s Patients with Episodes of Freezing of Gait

Using wavelet sub-band and fuzzy 2-partition entropy to segment chronic lymphocytic leukemia images

Pre-processing Techniques to Improve the Efficiency of Video Identification for the Pygmy Bluetongue Lizard

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