Impact of chromophores on colour appearance in a computational skin model

Early diagnosis of skin cancer offers the patient more favorable treatment options. Color fidelity of skin images is a major concern for dermatologists as adoption of digital dermatoscopes is increasing rapidly. Accurate color depiction of the lesion and surrounding skin are vital in diagnostic evaluation of a lesion. We previously introduced VCT-Derma, a pipeline for dermatological Virtual Clinical Trials (VCTs) including detailed and flexible models of human skin and lesions, which represent the patient in the entire dermatoscopy-based diagnostic process. However, those initial models of skin and lesions did not properly account for tissue colors. Our new skin model accounts for tissue color appearance by incorporating chromophores (e.g., melanin, blood) into the tissue model, and simulating the optical properties of the various skin layers. The physical properties of the skin and lesion were selected from clinically plausible values. The model and simulated dermatoscope images were created in open modelling software, assuming a linear camera model. We have assumed ambient white lighting, with a 6mm distance to the camera. Our model of color appearance was characterised by comparing the brightness of the lesion to its depth. The brightness of the lesion is compared through the variability of the mean gray values of a cropped region around the lesion. We compare two skin models, one without extensive chromophore content and one with. Our preliminary evaluation of increasing chromophore content shows promise based on the results presented here. Further refinement and validation of the model is ongoing

Investigating poisson noise filtering in Digital Breast Tomosynthesis

Digital Breast Tomosynthesis (DBT) is a potential\ud candidate to substitute digital mammography in breast cancer\ud screening. In DBT, projection images are acquired with low\ud levels of radiation, which significantly increases image noise. In\ud this work, we evaluate the effect of a denoising filter, designed for\ud digital mammography, on the reduction of quantum noise in\ud DBT images. This filter is based on an adaptive Wiener filter and\ud the Anscombe transformation, to reduce Poisson noise without\ud significantly affecting image sharpness. Denoising was applied to\ud a set of synthetic DBT images generated using a 3D\ud anthropomorphic software breast phantom. Images without noise\ud was also created to provide ground-truth information. In order to\ud evaluate the denoising performance in different steps of the DBT\ud imaging, filtering was applied separately to the projections\ud (before reconstruction) and to the tomographic slices (after\ud reconstruction). The performance of the filter was evaluated\ud considering qualitative and quantitative analysis of the images\ud before and after denoising.FAPESPCNP

Effect of denoising on the quality of reconstructed images in digital breast tomosynthesis

Individual projection images in Digital Breast Tomosynthesis (DBT) must be acquired with low levels of radiation,\ud which significantly increases image noise. This work investigates the influence of a denoising algorithm and the\ud Anscombe transformation on the reduction of quantum noise in DBT images. The Anscombe transformation is a\ud variance-stabilizing transformation that converts the signal-dependent quantum noise to an approximately signalindependent\ud Gaussian additive noise. Thus, this transformation allows for the use of conventional denoising algorithms,\ud designed for additive Gaussian noise, on the reduction of quantum noise, by working on the image in the Anscombe\ud domain. In this work, denoising was performed by an adaptive Wiener filter, previously developed for 2D\ud mammography, which was applied to a set of synthetic DBT images generated using a 3D anthropomorphic software\ud breast phantom. Ideal images without noise were also generated in order to provide a ground-truth reference. Denoising\ud was applied separately to DBT projections and to the reconstructed slices. The relative improvement in image quality\ud was assessed using objective image quality metrics, such as peak signal-to-noise ratio (PSNR) and mean structural\ud similarity index (SSIM). Results suggest that denoising works better for tomosynthesis when using the Anscombe\ud transformation and when denoising was applied to each projection image before reconstruction; in this case, an average\ud increase of 9.1 dB in PSNR and 58.3% in SSIM measurements was observed. No significant improvement was observed\ud by using the Anscombe transformation when denoising was applied to reconstructed images, suggesting that the\ud reconstruction algorithm modifies the noise properties of the DBT images.FAPESPCNP

Comparison of Methods for Classification of Breast Ductal Branching Patterns

Abstract. Topological properties of the breast ductal network have shown the potential for classifying clinical breast images with and without radiological findings. In this paper, we review three methods for the description and classification of breast ductal topology. The methods are based on ramification matrices and symbolic representation employing text mining techniques.. The performance of these methods has been compared using clinical x-ray and MR images of breast ductal networks. We observed the accuracy of the classification between the ductal trees segmented from the x-ray galactograms with radiological findings and normal cases in the range of 0.86-0.91%. The accuracy of the classification of the ductal trees segmented from the MR autogalactograms was observed in the range of 0.5-0.89%.

Pipeline for effective denoising of digital mammography and digital breast tomosynthesis

Denoising can be used as a tool to enhance image quality and enforce low radiation doses in X-ray medical imaging. The effectiveness of denoising techniques relies on the validity of the underlying noise model. In full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT), calibration steps like the detector offset and flat-fielding can affect some assumptions made by most denoising techniques. Furthermore, quantum noise found in X-ray images is signal-dependent and can only be treated by specific filters. In this work we propose a pipeline for FFDM and DBT image denoising that considers the calibration steps and simplifies the modeling of the noise statistics through variance-stabilizing transformations (VST). The performance of a state-of-the-art denoising method was tested with and without the proposed pipeline. To evaluate the method, objective metrics such as the normalized root mean square error (N-RMSE), noise power spectrum, modulation transfer function (MTF) and the frequency signal-to-noise ratio (SNR) were analyzed. Preliminary tests show that the pipeline improves denoising. When the pipeline is not used, bright pixels of the denoised image are under-filtered and dark pixels are over-smoothed due to the assumption of a signal-independent Gaussian model. The pipeline improved denoising up to 20% in terms of spatial N-RMSE and up to 15% in terms of frequency SNR. Besides improving the denoising, the pipeline does not increase signal smoothing significantly, as shown by the MTF. Thus, the proposed pipeline can be used with state-of-the-art denoising techniques to improve the quality of DBT and FFDM images.publishedVersionPeer reviewe