André A. Bell

High Dynamic Range Microscopy for Cytopathological Cancer Diagnosis

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.

Multispectral high dynamic range imaging

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.

Noise in high dynamic range imaging

High dynamic range (HDR) imaging is more and more widely used to increase the limited dynamic range of digital cameras and, in turn, to cover the dynamic range of the acquired scene. This image acquisition process can be subdivided into two steps. The first step is the measurement or estimation of the mostly non-linear camera transfer function (CTF). This is followed by the second step, the combination of a set of differently exposed images of the same scene into one HDR image after linearization with the inverse CTF. Each of the individual images in such an exposure set contains noise from the image acquisition process. Consequently, the calculated HDR image will as well contain noise, which fortunately is reduced by the weighted average of the images from the exposure set. We analyze the achieved gain in SNR for different weighting functions proposed in the literature and compare these with a plain average. Although these functions are based on reasonable intuitions, we show that the highest SNRgain is achieved with the plain average.

An Evaluation Framework for the Accuracy of Camera Transfer Functions Estimated from Differently Exposed Images

Intensity values read from CCD- or CMOS-cameras are usually not proportional to the irradiance acquired by the sensor, but are mapped by a mostly nonlinear camera transfer function (CTF). This CTF can be measured using a test chart. This, however, is afflicted with the difficulty of ensuring uniform illumination of the chart. An alternative to chart-based measurements of the CTF is to use differently exposed images of the same scene. In this paper, we describe a radiometry-based experimental setup to directly measure the CTF. We furthermore show how to obtain image pairs of known exposure ratios from the same experiment, i.e., under identical environmental circumstances (light, temperature, camera settings). We use these images to estimate the CTF on differently exposed images, thus enabling a quantitative comparison of estimated and measured CTF

High Dynamic Range Images as a Basis for Detection of Argyrophilic Nucleolar Organizer Regions Under Varying Stain Intensities

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.

Segmentation and Detection of Nuclei in Silver Stained Cell Specimens for Early Cancer Diagnosis

For successful cure, cancer has to be detected as early as possible. Since cancer starts from a single cell, this can best be done using cytopathological methods. One important diagnostically relevant measure is the proliferation rate of the cells, which can be estimated from segmented silver stained nuclei. However, the microscopy images of silver stained specimens vary strongly in intensity and contrast and are furthermore compromised by an overall texture. We show that a precise segmentation of the nuclei is possible using a two-step approach. First, an oversegmentation with the mean shift algorithm is obtained. In a second step, these regions are merged to objects, guided by a suitable shape model, viz an ellipse, but simultaneously allowing deviations from this shape model. The segmentation results are compared to a gold standard of 8617 nuclei from 23 specimens of the thyroid gland, achieving a mean areal segmentation error of DeltaA macrnucleus = 12mum2 per nucleus.

Towards Fully Automatic Acquisition of Multimodal Cytopathological Microscopy Images with Autofocus and Scene Managing

OBJECTIVES To increase the chance for a cure, cancer must be detected as early as possible. This can be achieved with cytopathological diagnostic methods. For a further increase of the diagnostic accuracy of these methods we introduced the multimodal cell analysis, viz, cells on the slide have to be relocalized to enable successive analysis of identical cells in different stains. For practical reasons the relocalization step must be automated. METHODS For a fully automatic acquisition of successive cell images we use a passive autofocus that is adaptive to the material, i.e., to the cells, followed by a comparison of the scenes, i.e., the cell constellation, of two such obtained images from different stains. In case that no sub-scene match can be found the search is extended to the surrounding area. A set of 1556 scenes from seven specimens have been subject to our algorithm. The automatically relocalized and acquired images from a second stain have been manually compared to the images from a first stain. RESULTS An overall relocalization rate of 85.4% is achieved. 14.3% of the images could not be relocalized and are lost for the following diagnostic process, while the critical case of erroneously matched images was observed in only 0.3% of cases. CONCLUSIONS We could show that it is possible to automatically acquire images of successive stains of identical cells on cytopathological specimens. The method presented achieves acceptable relocalization rates. Wrong image acquisitions are very rare and can mostly be ascribed to images with single cells, i.e., without scene information.

HDR-Microscopy of Cell Specimens: Imaging and Image Analysis

Bright field microscopy is applied in both biomedical research and diagnostics. The cellular material is generally imaged with cameras mounted onto the microscope, allowing thus to measure cell properties. However, these cameras exhibit only a limited dynamic range, which makes the quantification of these properties difficult or even impossible. We therefore show how the dynamic range can be increased by acquiring a set of differently exposed images of a specimen. These high dynamic range (HDR) images allow to measure features, which 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 measurements of colors. We develop HDR microscopy-based algorithms for three different applications essential for early cancer diagnostics, viz, detection of positive cells in immunocytochemical marker stained material, nuclei segmentation in low contrast morphologically stained material, and detection of certain subcellular features, so- called AgNORs, in silver (Ag) stained specimens.

Automatic scene comparison and matching in multimodal cytopathological microscopic images

The potential of cytopathologic diagnoses to detect cancer of a variety of types non-invasively, cost-efficiently and up to three years ahead of conventional histopathologic diagnoses can be increased by the application of adjuvant methods, i.e. the combination of different stainings. For further improvement of cytopathology we introduced multimodal cell analysis (MMCA) which combines specific information about identical cells in different stainings successively applied to the same microscope slide. This requires a precise relocation and coregistration of individual cells under scrutiny. As a precondition for application in daily routine and screening settings the crucial relocation of cells has to be automated. The paper describes a method for an automatic retrieval of images of cells which have already been selected and recorded in a preceding staining together with their coordinates. Due to inevitable mechanical inaccuracies the geometric match is insufficient. The comparison of nuclear constellations based on segmentations in both stains facilitates an automatic correction of the position even if there is a subscene matching only. The process furthermore generates the initial guess for the succeeding coregistration which thereby gains robustness. The success rate of the method described is about 85%.

Computer aided cytological cancer diagnosis : Cell type classification as a step towards fully automatic cancer diagnostics on cytopathological specimens of serous effusions

Compared to histopathological methods cancer can be detected earlier, specimens can be obtained easier and with less discomfort for the patient by cytopathological methods. Their downside is the time needed by an expert to find and select the cells to be analyzed on a specimen. To increase the use of cytopathological diagnostics, the cytopathologist has to be supported in this task. DNA image cytometry (DNA-ICM) is one important cytopathological method that measures the DNA content of cells based on the absorption of light within Feulgen stained cells. The decision whether or not the patient has cancer is based on the histogram of the DNA values. To support the cytopathologist it is desirable to replace manual screening of the specimens by an automatic selection of relevant cells for DNA-ICM. This includes automated acquisition and segmentation of focused cells, a recognition of cell types, and a selection of cells to be measured. As a step towards automated cell type detection we show the discrimination of cell types in serous effusions on a selection of about 3, 100 manually classified cells. We present a set of 112 features and the results of feature selection with ranking and a floating-search method combined with different objective functions. The validation of the best feature sets with a k-nearest neighbor and a fuzzy k-nearest neighbor classifier on a disjoint set of cells resulted in classification rates of 96% for lymphocytes and 96.8% for the diagnostically relevant cells (mesothelial+ cells), which includes benign and malign mesothelial cells and metastatic cancer cells.