1
|
Halimi A, Batatia H, Le Digabel J, Josse G, Tourneret JY. Wavelet-based statistical classification of skin images acquired with reflectance confocal microscopy. Biomed Opt Express 2017; 8:5450-5467. [PMID: 29296480 PMCID: PMC5745095 DOI: 10.1364/boe.8.005450] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 10/18/2017] [Accepted: 10/19/2017] [Indexed: 06/07/2023]
Abstract
Detecting skin lentigo in reflectance confocal microscopy images is an important and challenging problem. This imaging modality has not yet been widely investigated for this problem and there are a few automatic processing techniques. They are mostly based on machine learning approaches and rely on numerous classical image features that lead to high computational costs given the very large resolution of these images. This paper presents a detection method with very low computational complexity that is able to identify the skin depth at which the lentigo can be detected. The proposed method performs multiresolution decomposition of the image obtained at each skin depth. The distribution of image pixels at a given depth can be approximated accurately by a generalized Gaussian distribution whose parameters depend on the decomposition scale, resulting in a very-low-dimension parameter space. SVM classifiers are then investigated to classify the scale parameter of this distribution allowing real-time detection of lentigo. The method is applied to 45 healthy and lentigo patients from a clinical study, where sensitivity of 81.4% and specificity of 83.3% are achieved. Our results show that lentigo is identifiable at depths between 50μm and 60μm, corresponding to the average location of the the dermoepidermal junction. This result is in agreement with the clinical practices that characterize the lentigo by assessing the disorganization of the dermoepidermal junction.
Collapse
Affiliation(s)
- Abdelghafour Halimi
- University of Toulouse, IRIT-INPT, 2 rue Camichel, BP 7122, 31071 Toulouse cedex 7,
France
| | - Hadj Batatia
- University of Toulouse, IRIT-INPT, 2 rue Camichel, BP 7122, 31071 Toulouse cedex 7,
France
| | - Jimmy Le Digabel
- Centre de Recherche sur la Peau, Pierre Fabre Dermo-Cosmétique, 2 rue Viguerie, 31025 Toulouse Cedex 3, France
| | - Gwendal Josse
- Centre de Recherche sur la Peau, Pierre Fabre Dermo-Cosmétique, 2 rue Viguerie, 31025 Toulouse Cedex 3, France
| | - Jean Yves Tourneret
- University of Toulouse, IRIT-INPT, 2 rue Camichel, BP 7122, 31071 Toulouse cedex 7,
France
| |
Collapse
|
2
|
Ho D, Drake TK, Bentley RC, Valea FA, Wax A. Evaluation of hybrid algorithm for analysis of scattered light using ex vivo nuclear morphology measurements of cervical epithelium. Biomed Opt Express 2015; 6:2755-65. [PMID: 26309741 PMCID: PMC4541505 DOI: 10.1364/boe.6.002755] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2015] [Revised: 06/29/2015] [Accepted: 07/01/2015] [Indexed: 05/07/2023]
Abstract
We evaluate a new hybrid algorithm for determining nuclear morphology using angle-resolved low coherence interferometry (a/LCI) measurements in ex vivo cervical tissue. The algorithm combines Mie theory based and continuous wavelet transform inverse light scattering analysis. The hybrid algorithm was validated and compared to traditional Mie theory based analysis using an ex vivo tissue data set. The hybrid algorithm achieved 100% agreement with pathology in distinguishing dysplastic and non-dysplastic biopsy sites in the pilot study. Significantly, the new algorithm performed over four times faster than traditional Mie theory based analysis.
Collapse
Affiliation(s)
- Derek Ho
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Tyler K. Drake
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Rex C. Bentley
- Department of Pathology, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Fidel A. Valea
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Adam Wax
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| |
Collapse
|
3
|
Bal U. Non-contact estimation of heart rate and oxygen saturation using ambient light. Biomed Opt Express 2015; 6:86-97. [PMID: 25657877 PMCID: PMC4317113 DOI: 10.1364/boe.6.000086] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2014] [Revised: 11/19/2014] [Accepted: 12/01/2014] [Indexed: 05/22/2023]
Abstract
We propose a robust method for automated computation of heart rate (HR) from digital color video recordings of the human face. In order to extract photoplethysmographic signals, two orthogonal vectors of RGB color space are used. We used a dual tree complex wavelet transform based denoising algorithm to reduce artifacts (e.g. artificial lighting, movement, etc.). Most of the previous work on skin color based HR estimation performed experiments with healthy volunteers and focused to solve motion artifacts. In addition to healthy volunteers we performed experiments with child patients in pediatric intensive care units. In order to investigate the possible factors that affect the non-contact HR monitoring in a clinical environment, we studied the relation between hemoglobin levels and HR estimation errors. Low hemoglobin causes underestimation of HR. Nevertheless, we conclude that our method can provide acceptable accuracy to estimate mean HR of patients in a clinical environment, where the measurements can be performed remotely. In addition to mean heart rate estimation, we performed experiments to estimate oxygen saturation. We observed strong correlations between our SpO2 estimations and the commercial oximeter readings.
Collapse
Affiliation(s)
- Ufuk Bal
- Faculty of Engineering, Muğla Sıtkı Koçman University, 48000 Kötekli/Muğla Turkey
| |
Collapse
|
4
|
Denstedt M, Bjorgan A, Milanič M, Randeberg LL. Wavelet based feature extraction and visualization in hyperspectral tissue characterization. Biomed Opt Express 2014; 5:4260-80. [PMID: 25574437 PMCID: PMC4285604 DOI: 10.1364/boe.5.004260] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2014] [Revised: 11/02/2014] [Accepted: 11/04/2014] [Indexed: 05/12/2023]
Abstract
Hyperspectral images of tissue contain extensive and complex information relevant for clinical applications. In this work, wavelet decomposition is explored for feature extraction from such data. Wavelet methods are simple and computationally effective, and can be implemented in real-time. The aim of this study was to correlate results from wavelet decomposition in the spectral domain with physical parameters (tissue oxygenation, blood and melanin content). Wavelet decomposition was tested on Monte Carlo simulations, measurements of a tissue phantom and hyperspectral data from a human volunteer during an occlusion experiment. Reflectance spectra were decomposed, and the coefficients were correlated to tissue parameters. This approach was used to identify wavelet components that can be utilized to map levels of blood, melanin and oxygen saturation. The results show a significant correlation (p <0.02) between the chosen tissue parameters and the selected wavelet components. The tissue parameters could be mapped using a subset of the calculated components due to redundancy in spectral information. Vessel structures are well visualized. Wavelet analysis appears as a promising tool for extraction of spectral features in skin. Future studies will aim at developing quantitative mapping of optical properties based on wavelet decomposition.
Collapse
Affiliation(s)
- Martin Denstedt
- Dept. of Electronics and Telecommunications, Norwegian University of Science and Technology, 7491 Trondheim,
Norway
| | - Asgeir Bjorgan
- Dept. of Electronics and Telecommunications, Norwegian University of Science and Technology, 7491 Trondheim,
Norway
| | - Matija Milanič
- Dept. of Electronics and Telecommunications, Norwegian University of Science and Technology, 7491 Trondheim,
Norway
| | - Lise Lyngsnes Randeberg
- Dept. of Electronics and Telecommunications, Norwegian University of Science and Technology, 7491 Trondheim,
Norway
| |
Collapse
|
5
|
Zhu H, Poostchi A, Vernon SA, Crabb DP. Detecting abnormality in optic nerve head images using a feature extraction analysis. Biomed Opt Express 2014; 5:2215-2230. [PMID: 25071960 PMCID: PMC4102360 DOI: 10.1364/boe.5.002215] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2014] [Revised: 06/04/2014] [Accepted: 06/04/2014] [Indexed: 06/03/2023]
Abstract
Imaging and evaluation of the optic nerve head (ONH) plays an essential part in the detection and clinical management of glaucoma. The morphological characteristics of ONHs vary greatly from person to person and this variability means it is difficult to quantify them in a standardized way. We developed and evaluated a feature extraction approach using shift-invariant wavelet packet and kernel principal component analysis to quantify the shape features in ONH images acquired by scanning laser ophthalmoscopy (Heidelberg Retina Tomograph [HRT]). The methods were developed and tested on 1996 eyes from three different clinical centers. A shape abnormality score (SAS) was developed from extracted features using a Gaussian process to identify glaucomatous abnormality. SAS can be used as a diagnostic index to quantify the overall likelihood of ONH abnormality. Maps showing areas of likely abnormality within the ONH were also derived. Diagnostic performance of the technique, as estimated by ROC analysis, was significantly better than the classification tools currently used in the HRT software - the technique offers the additional advantage of working with all images and is fully automated.
Collapse
Affiliation(s)
- Haogang Zhu
- School of Health Sciences, City University London, London, UK
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital and University College London Institute of Ophthalmology, UK, London, UK
| | | | - Stephen A Vernon
- Nottingham University Hospitals, Nottingham, UK
- Department of Ophthalmology, University of Nottingham, Nottingham, UK
| | - David P Crabb
- School of Health Sciences, City University London, London, UK
| |
Collapse
|
6
|
Bal U. Dual tree complex wavelet transform based denoising of optical microscopy images. Biomed Opt Express 2012; 3:3231-9. [PMID: 23243573 PMCID: PMC3521299 DOI: 10.1364/boe.3.003231] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2012] [Revised: 11/07/2012] [Accepted: 11/09/2012] [Indexed: 05/26/2023]
Abstract
Photon shot noise is the main noise source of optical microscopy images and can be modeled by a Poisson process. Several discrete wavelet transform based methods have been proposed in the literature for denoising images corrupted by Poisson noise. However, the discrete wavelet transform (DWT) has disadvantages such as shift variance, aliasing, and lack of directional selectivity. To overcome these problems, a dual tree complex wavelet transform is used in our proposed denoising algorithm. Our denoising algorithm is based on the assumption that for the Poisson noise case threshold values for wavelet coefficients can be estimated from the approximation coefficients. Our proposed method was compared with one of the state of the art denoising algorithms. Better results were obtained by using the proposed algorithm in terms of image quality metrics. Furthermore, the contrast enhancement effect of the proposed method on collagen fıber images is examined. Our method allows fast and efficient enhancement of images obtained under low light intensity conditions.
Collapse
Affiliation(s)
- Ufuk Bal
- Faculty of Technology, Muğla Sıtkı Koçman University, 48000 Kötekli/Muğla, Turkey
| |
Collapse
|
7
|
Mayer MA, Borsdorf A, Wagner M, Hornegger J, Mardin CY, Tornow RP. Wavelet denoising of multiframe optical coherence tomography data. Biomed Opt Express 2012; 3:572-89. [PMID: 22435103 PMCID: PMC3296543 DOI: 10.1364/boe.3.000572] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2011] [Revised: 01/18/2012] [Accepted: 01/20/2012] [Indexed: 05/13/2023]
Abstract
We introduce a novel speckle noise reduction algorithm for OCT images. Contrary to present approaches, the algorithm does not rely on simple averaging of multiple image frames or denoising on the final averaged image. Instead it uses wavelet decompositions of the single frames for a local noise and structure estimation. Based on this analysis, the wavelet detail coefficients are weighted, averaged and reconstructed. At a signal-to-noise gain at about 100% we observe only a minor sharpness decrease, as measured by a full-width-half-maximum reduction of 10.5%. While a similar signal-to-noise gain would require averaging of 29 frames, we achieve this result using only 8 frames as input to the algorithm. A possible application of the proposed algorithm is preprocessing in retinal structure segmentation algorithms, to allow a better differentiation between real tissue information and unwanted speckle noise.
Collapse
Affiliation(s)
- Markus A. Mayer
- Pattern Recognition Lab, Martensstrasse 3, 91058 Erlangen,
Germany
- Erlangen Graduate School in Advanced Optical Technologies (SAOT), Paul-Gordan Str. 6, 91052 Erlangen,
Germany
| | - Anja Borsdorf
- Pattern Recognition Lab, Martensstrasse 3, 91058 Erlangen,
Germany
| | - Martin Wagner
- University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg,
Germany
| | - Joachim Hornegger
- Pattern Recognition Lab, Martensstrasse 3, 91058 Erlangen,
Germany
- Erlangen Graduate School in Advanced Optical Technologies (SAOT), Paul-Gordan Str. 6, 91052 Erlangen,
Germany
| | | | - Ralf P. Tornow
- Department of Ophthalmology, Schwabachanlage 6, 91054 Erlangen,
Germany
| |
Collapse
|