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André A. Bell

Bio: André A. Bell is an academic researcher from RWTH Aachen University. The author has contributed to research in topics: High dynamic range & High-dynamic-range imaging. The author has an hindex of 9, co-authored 23 publications receiving 208 citations.

Papers
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Journal ArticleDOI
TL;DR: This report describes the first clinical application of semiautomated multimodal cell analysis (MMCA), a novel technique for the early detection of cancer for cases with a limited number of suspicious cells, applied to oral cancer diagnostics on brush biopsies.
Abstract: BACKGROUND: This report describes what to the authors' knowledge is the first clinical application of semiautomated multimodal cell analysis (MMCA), a novel technique for the early detection of cancer for cases with a limited number of suspicious cells. In this clinical study, MMCA was applied to oral cancer diagnostics on brush biopsies. The MMCA approach was based on the sequential application of multiple stainings of identical, slide-based cells and repeated relocalizations and measurements of their diagnostic features, resulting in multiparametric features of individual cells. Data integration of the variously stained cells increased diagnostic accuracy. The implementation of MMCA also enabled fully automatic, adaptive image preprocessing, including registration of multimodal images and segmentation of cell nuclei. METHODS: In a preliminary clinical trial, 47 slides from brush biopsies of suspicious oral lesions were analyzed. The final histologic diagnoses included 20 squamous cell carcinomas, 7 hyperkeratotic leukoplakias, and 20 lichen planus mucosae. RESULTS: The stepwise application of 2 additional approaches (morphology, DNA content, argyrophilic nucleolar organizer region counts) increased the specificity of conventional cytologic diagnosis from 92.6% to 100%. This feasibility study provided a proof of concept, demonstrating efficiency, robustness, and diagnostic accuracy on slide-based cytologic specimens. CONCLUSIONS: The authors concluded that MMCA may become a sensitive and highly specific, objective, and reproducible adjuvant diagnostic tool for the identification of neoplastic changes in oral smears that contain only a few abnormal cells. Cancer (Cancer Cytopathol) 2009. © 2009 American Cancer Society.

41 citations

Proceedings ArticleDOI
27 Jan 2008
TL;DR: This work presents a promising combination of both technologies, a high dynamic range multispectral camera featuring a higher color accuracy, an improved signal to noise ratio and greater dynamic range compared to a similar low dynamic range camera.
Abstract: Capturing natural scenes with high dynamic range content using conventional RGB cameras generally results in saturated and underexposed and therefore compromising image areas. Furthermore the image lacks color accuracy due to a systematic color error of the RGB color filters. The problem of the limited dynamic range of the camera has been addressed by high dynamic range imaging1, 2 (HDRI): Several RGB images of different exposures are combined into one image with greater dynamic range. Color accuracy on the other hand can be greatly improved using multispectral cameras,3 which more accurately sample the electromagnetic spectrum. We present a promising combination of both technologies, a high dynamic range multispectral camera featuring a higher color accuracy, an improved signal to noise ratio and greater dynamic range compared to a similar low dynamic range camera.© (2008) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

33 citations

Journal ArticleDOI
TL;DR: It is shown how the dynamic range can be increased by acquiring a set of differently exposed cell images, which allow to measure cellular features that are otherwise difficult to capture, if at all, in high dynamic range (HDR) images.
Abstract: Cancer is one of the most common causes of death. Cytopathological, i.e., cell-based, diagnosis of cancer can be applied in screening scenarios and allows an early and highly sensitive detection of cancer, thus increasing the chance for cure. The detection of cancer on cells addressed in this paper is based on bright field light microscopy. The cells are imaged with a camera mounted on a microscope, allowing to measure cell properties. However, these cameras exhibit only a limited dynamic range, which often makes the quantification of properties difficult or even impossible. Consequently, to allow a computer-assisted analysis of microscopy images, the imaging has to be improved. To this end, we show how the dynamic range can be increased by acquiring a set of differently exposed cell images. These high dynamic range (HDR) images allow to measure cellular features that are otherwise difficult to capture, if at all. We show that HDR microscopy not only increases the dynamic range, but furthermore reduces noise and improves the acquisition of colors. We develop HDR microscopy-based algorithms, which are essential for cytopathological oncology and early cancer detection and only possible with HDR microscopy imaging. We show the detection of certain subcellular features, so-called AgNORs, in silver (Ag) stained specimens. Furthermore, we give examples of two further applications, namely: 1) the detection of stained cells in immunocytochemical preparations and 2) color separation for nuclear segmentation of specimens stained with low contrast.

23 citations

Proceedings Article
01 Jan 2008
TL;DR: This paper discusses and evaluates different optimization strategies for mean shift based image segmentation, and compares segmentation results of heuristic-based, performance-optimized implementations with the segmentation result of the original mean shift algorithm as a gold standard.
Abstract: The mean shift algorithm is a powerful clustering technique, which is based on an iterative scheme to detect modes in a probability density function. It has been utilized for image segmentation by seeking the modes in a feature space composed of spatial and color information. Although the modes of the feature space can be efficiently calculated in that scheme, different optimization techniques have been investigated to further improve the calculation speed. Besides those techniques that improve the efficiency using specialized data structures, there are other ones, which take advantage of some heuristics, and therefore affect the accuracy of the algorithm output. In this paper we discuss and evaluate different optimization strategies for mean shift based image segmentation. These optimization techniques are quantitatively evaluated based on different real world images. We compare segmentation results of heuristic-based, performance-optimized implementations with the segmentation result of the original mean shift algorithm as a gold standard. Towards this end, we utilize different partition distance measures, by identifying corresponding regions and analyzing the thus revealed differences.

20 citations

Proceedings ArticleDOI
01 Oct 2006
TL;DR: It is shown how high dynamic range images of nuclei can help to correctly segment the so-called argyrophilic nucleolar organizer regions (AgNORs), which appear as spot-like areas darker than their immediate surroundings.
Abstract: Silver staining of cytopathologic specimens offers advantages in cancer diagnostics A difficulty with such stained cell specimens is the very high dynamic range needed by the imaging system to appropriately cover the varying stain intensities Beside those images of cell nuclei that can be used for the diagnostic interpretation, there are nuclei that appear too dark to observe their relevant properties, the so-called argyrophilic nucleolar organizer regions (AgNORs), which appear as spot-like areas darker than their immediate surroundings We therefore show how high dynamic range images of nuclei can help to correctly segment the AgNORs To this end, we acquire a sequence of differently exposed images, which are then combined into a high dynamic range image Based on the dynamic range of the image signal within the segmented cell area, we compute another image which provides optimal contrast over this area of interest To further increase the contrast for dark objects, a suitable nonlinear point transform is simultaneously applied We provide examples of the thus generated images and their corresponding segmentations

17 citations


Cited by
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Book ChapterDOI
E.R. Davies1
01 Jan 1990
TL;DR: This chapter introduces the subject of statistical pattern recognition (SPR) by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier.
Abstract: This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

1,189 citations

Journal ArticleDOI

1,008 citations

Book ChapterDOI
23 Apr 2010
TL;DR: A number of clinical trials of targeted therapies in HNSCC show promise in the control of both advanced loco-regional disease and relapsed/metastatic disease.
Abstract: The following sites are included: Lip, oral cavity Pharynx: Oropharynx, nasopharynx, hypopharynx Larynx: Supraglottis, glottis, subglottis Maxillary and ethmoid sinus Salivary glands Thyroid gland

256 citations

Proceedings ArticleDOI
13 Jun 2010
TL;DR: This work proposes a weighting function that produces statistically optimal estimates under the assumption of compound-Gaussian noise, based on a calibrated camera model that accounts for all noise sources and allows us to simultaneously estimate the irradiance and its uncertainty.
Abstract: Given a multi-exposure sequence of a scene, our aim is to recover the absolute irradiance falling onto a linear camera sensor. The established approach is to perform a weighted average of the scaled input exposures. However, there is no clear consensus on the appropriate weighting to use. We propose a weighting function that produces statistically optimal estimates under the assumption of compound-Gaussian noise. Our weighting is based on a calibrated camera model that accounts for all noise sources. This model also allows us to simultaneously estimate the irradiance and its uncertainty. We evaluate our method on simulated and real world photographs, and show that we consistently improve the signal-to-noise ratio over previous approaches. Finally, we show the effectiveness of our model for optimal exposure sequence selection and HDR image denoising.

197 citations

Journal ArticleDOI
TL;DR: Diagnostic tests for early detection include brush biopsy, toluidine blue staining, autofluorescence, salivary proteomics, DNA analysis, biomarkers and spectroscopy, which critically examines their value in identifying oral squamous cell carcinoma and its precursor lesions.
Abstract: The prognosis for patients with oral squamous cell carcinoma remains poor in spite of advances in therapy of many other malignancies. Early diagnosis and treatment remains the key to improved patient survival. Because the scalpel biopsy for diagnosis is invasive and has potential morbidity, it is reserved for evaluating highly suspicious lesions and not for the majority of oral lesions which are clinically not suspicious. Furthermore, scalpel biopsy has significant interobserver and intraobserver variability in the histologic diagnosis of dysplasia. There is an urgent need to devise critical diagnostic tools for early detection of oral dysplasia and malignancy that are practical, noninvasive and can be easily performed in an out-patient set-up. Diagnostic tests for early detection include brush biopsy, toluidine blue staining, autofluorescence, salivary proteomics, DNA analysis, biomarkers and spectroscopy. This state of the art review critically examines these tests and assesses their value in identifying oral squamous cell carcinoma and its precursor lesions.

167 citations