Brian D'Alessandro

Optical Imaging Modalities for Biomedical Applications

Optical photographic imaging is a well known imaging method that has been successfully translated into biomedical applications such as microscopy and endoscopy. Although several advanced medical imaging modalities are used today to acquire anatomical, physiological, metabolic, and functional information from the human body, optical imaging modalities including optical coherence tomography, confocal microscopy, multiphoton microscopy, multispectral endoscopy, and diffuse reflectance imaging have recently emerged with significant potential for non-invasive, portable, and cost-effective imaging for biomedical applications spanning tissue, cellular, and molecular levels. This paper reviews methods for modeling the propagation of light photons in a biological medium, as well as optical imaging from organ to cellular levels using visible and near-infrared wavelengths for biomedical and clinical applications.

Mp3 bit rate quality detection through frequency spectrum analysis

The proliferation of the lossy MP3 format as the standard for audio transferred over the internet is of great concern to audiophiles, those who deeply care about good audio quality. Typically, the bit rate of an MP3 file is used as a relative measure of audio quality, however, this check fails if the audio has been transcoded from a lower bit rate to a higher bit rate. In this paper, we propose a method of detecting the original lower bit rate of a given audio file by analyzing its high frequency spectrum. Using a Support Vector Machine classifier and five classes of bit rates (CBR 128 kbps, 192 kbps, 256 kbps, 320 kbps, and VBR-0), our algorithm returned an average success rate of 97% in correctly detecting the original compressed bit rate of an audio file in the absence of any coding format knowledge other than the audio signal itself. Furthermore, our algorithm also detected the original lower bit rates of 320 kbps MP3s transcoded from 128 kbps and 192 kbps sources with a success rate of 99%.

3-D Volume Reconstruction of Skin Lesions for Melanin and Blood Volume Estimation and Lesion Severity Analysis

Subsurface information about skin lesions, such as the blood volume beneath the lesion, is important for the analysis of lesion severity towards early detection of skin cancer such as malignant melanoma. Depth information can be obtained from diffuse reflectance based multispectral transillumination images of the skin. An inverse volume reconstruction method is presented which uses a genetic algorithm optimization procedure with a novel population initialization routine and nudge operator based on the multispectral images to reconstruct the melanin and blood layer volume components. Forward model evaluation for fitness calculation is performed using a parallel processing voxel-based Monte Carlo simulation of light in skin. Reconstruction results for simulated lesions show excellent volume accuracy. Preliminary validation is also done using a set of 14 clinical lesions, categorized into lesion severity by an expert dermatologist. Using two features, the average blood layer thickness and the ratio of blood volume to total lesion volume, the lesions can be classified into mild and moderate/severe classes with 100% accuracy. The method therefore has excellent potential for detection and analysis of pre- malignant lesions.

Multispectral optical imaging of skin-lesions for detection of malignant melanomas

Optical imaging of skin-lesions for early detection and management of the most fatal skin-cancer malignant melanoma is of significant interest in mass screening of skin-lesions with high-risk population. Surface illumination based optical imaging methods such as epiluminescence light microscopy (ELM) through “Dermascopy” has shown a significant potential in improving early diagnosis of malignant melanomas. Limitations of surface reflectance based imaging systems have been realized in analyzing images for important vascular and depth dependent information. We have developed a novel optical imaging system, the Nevoscope, that uses multispectral transillumination as to provide images of skin-lesions showing sub-surface pigmentation as well as vascular architecture based blood volume information. This paper presents multispectral Nevoscope transillumination method to compare and analyze ratiometric measurements to epiluminescence imaging for its ability to discriminate malignant melanomas from dysplastic nevi and other normal skin-lesions.

Depth-Dependent Hemoglobin Analysis From Multispectral Transillumination Images

Multispectral transillumination imaging is a promising modality for noninvasive imaging of living tissue. Multispectral Nevoscope imaging is directed toward the imaging of skin lesions for the detection and characterization of skin cancers through the volumetric analysis of selected chromophores, such as melanin, oxy-, and deoxyhemoglobin. In this letter, we present a novel method of recovering depth-dependent measurements from transillumination images obtained through the Nevoscope. A method for estimating the depth-dependent point spread function is presented and used in recovering multispectral transillumination images of a skin phantom or lesion through blind deconvolution. A method for ratiometric analysis for the quantification of oxy- and deoxyhemoglobin is then presented and evaluated on a skin phantom. The presented methods would allow reliable quantitative analysis of multispectral Nevoscope images for early detection of angiogenesis leading to early diagnosis of skin cancers.

Voxel-based, parallel simulation of light in skin tissue for the reconstruction of subsurface skin lesion volumes

Early detection and diagnosis of skin cancer is essential to treating the malignancy and preventing death. Subsurface features and depth information are critical in evaluating a skin lesion for this early malignancy screening. We present a novel voxel-based Monte Carlo simulation of light propagation in skin tissue which runs in a highly parallel environment on desktop graphics processors, resulting in an extremely fast simulation of millions of photons in less than one second. We then use this model in a genetic algorithm for the inverse 3D volume reconstruction of a skin lesion, given a set of multispectral images obtained using non-invasive transillumination imaging. Our method demonstrates improved accuracy at a superior resolution to existing methods.

Blood oxygen saturation estimation in transilluminated images of skin lesions

Estimating the oxygen saturation of blood surrounding skin lesions is critical for the early detection of skin cancer and malignancy such as melanoma. Through multispectral transillumination of the skin, a set of images can be obtained which helps visualize areas of subsurface absorption due to chromophores such as melanin, hemoglobin, and deoxyhemoglobin. To relate pixel intensity in these images to spatially located absorption coefficient values, we develop a correction factor to Beers law, estimated through a Monte Carlo simulation of light propagation in skin, which takes into account the specific geometry of our transillumination imaging apparatus. We then use this relation on the multispectral imaging set for chromophore separation and oxygen saturation estimation. The separation method is validated through Monte Carlo simulation, as well as on a skin phantom. Results show that subsurface oxygen saturation can be reasonably estimated with good implications for the reconstruction of 3D skin lesion volumes using transillumination towards early detection of malignancy.

Computer aided analysis of epi-illumination and transillumination images of skin lesions for diagnosis of skin cancers

Skin lesion pigmentation area from surface, or, epi-illumination (ELM) images and blood volume area from transillumination (TLM) images are useful features to aid a dermatologist in the diagnosis of melanoma and other skin cancers in early curable stages. However, segmentation of these areas is difficult. In this work, we present an automatic segmentation tool for ELM and TLM images that also provides additional choices for user selection and interaction with adaptive learning. Our tool uses a combination of k-means clustering, wavelet analysis, and morphological operations to segment the lesion and blood volume, and then presents the user with six segmentation suggestions for both ELM and TLM images. The final selection of segmentation boundary may then be iteratively improved through scoring by multiple users. The ratio of TLM to ELM segmented areas is an indicator of dysplasia in skin lesions for detection of skin cancers, and this ratio is found to show a statistically significant trend in association with lesion dysplasia on a set of 81 pathologically validated lesions (p = 0.0058). We then present a support vector machine classifier using the results from the interactive segmentation method along with ratio, color, texture, and shape features to characterize skin lesions into three degrees of dysplasia with promising accuracy.

Multispectral transillumination imaging of skin lesions for oxygenated and deoxygenated hemoglobin measurement

The early detection of melanoma is critical for patient survival. One of the indentifying features of new malignancy is increased blood flow to the lesion. Multispectral transillumination using the Nevoscope has been demonstrated to be an effective tool for imaging the sub-surface vascular architecture of skin lesions. Using multispectral images obtained from this tool in the visible and near-infrared range, as well as the relative difference in spectral absorption due to oxyhemoglobin and deoxyhemoglobin, we propose an empirical method to estimate the blood flow volume within a skin lesion. From the images, estimates of the distribution of both Hb and HbO2 are calculated along with a ratiometric feature describing the relative oxygen saturation level in the blood. We validate our proposed method through the imaging of a skin phantom with embedded capillaries which can be filled with either an artificial Hb or HbO2 liquid. Our near-IR, multispectral computations nicely differentiate the Hb filled phantom versus the HbO2 filled phantom, demonstrating that these chromophores can be successfully separated and individually characterized for use in estimating the relative oxygen saturation of skin tissue.

Multi-parameter segmentation of brain images

Recent advances in multi-parameter MR brain imaging has enabled multi-class tissue characterization for better quantitative analysis and understanding brain disorders and pathologies. This paper presents a maximum likelihood based method for multi-class segmentation that utilizes spatio-frequency features obtained from wavelet analysis along with the multi-parameter measurements. Results on MR brain images of a patient with stroke are presented.