Realistic optical cell modeling and diffraction imaging simulation for study of optical and morphological parameters of nucleus

Coherent light scattering presents complex spatial patterns that depend on morphological and molecular features of biological cells. We present a numerical approach to establish realistic optical cell models for generating virtual cells and accurate simulation of diffraction images that are comparable to measured data of prostate cells. With a contourlet transform algorithm, it has been shown that the simulated images and extracted parameters can be used to distinguish virtual cells of different nuclear volumes and refractive indices against the orientation variation. These results demonstrate significance of the new approach for development of rapid cell assay methods through diffraction imaging.ECU Open Access Publishing Support Fun

Fast algorithms for histogram matching: Application to texture synthesis

Texture synthesis is the ability to create ensembles of images of similar structures from sample textures that have been photographed. The method we employ for texture synthesis is based on histogram matching of images at multiple scales and orientations. This paper reports two fast and in one case simple algorithms for histogram matching We show that the sort-matching and the optimal cumulative distribution function (CDF)-matching (OCM) algorithms provide high computational speed compared to that provided by the conventional approach. The sort-matching algorithm also provides exact histogram matching. Results of texture synthesis using either method show no subjective perceptual differences. The sort-matching algorithm is attractive because of its simplicity and speed, however as the size of the image increases, the OCM algorithm may be preferred for optimal computational speed

Computer aided diagnosis in radiology

Ankara : The Department of Electrical and Electronics Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1999.Thesis (Ph.D.) -- Bilkent University, 1999.Includes bibliographical references leaves 117-124.Breast cancer is one of the most deadly diseases for middle-aged women. In this thesis, computer-aided diagnosis tools are developed for the detection of breast cancer on mammograms. These tools include a detection scheme for microcalcification clusters which are an early sign of breast cancer, and a method to detect the boundaries of mass lesions. In the first microcalcification detection method we propose, a subband decomposition structure is employed. Contrary to the previous work, the detection is carried out in the subband domain. The mammogram image is first processed by a subband decomposition filter bank. The resulting subimage is analyzed to detect microcalcification clusters. In regions corresponding to the healthy breast tissue the distribution is almost Gaussian. Since microcalcifications are small, isolated bright spots, they produce outliers in the subimages and the distribution of pixels deviates from Gaussian. The subimages are divided into overlapping square regions. In each square region, skewness and kurtosis values are estimated. As third and fourth order correlation parameters, skewness and kurtosis, are measures of the asymmetry and impulsiveness of the distribution, they can be used to find the locations of microcalcification clusters. If the values of these parameters are higher than experimentally determined thresholds then the region is marked as a potential cancer area. Experimental studies indicate that this method successfully detects regions containing microcalcifications. We also propose another microcalcification detection method which uses two- dimensional (2-D) adaptive filtering and a higher order statistics based Gaussianity test. In this method, statistics of the prediction errors are computed to determine whether the samples are from a Gaussian distribution. The prediction error sequence deviates from Gaussianity around microcalcification locations because prediction of microcalcification pixels is more difficult than prediction of the pixels corresponding to healthy breast tissue. Then, we develop a new Gaussianity test which has higher sensitivity to outliers. The scheme which uses this test gives better detection performance compared to the previously proposed methods. Within the detected regions it is possible to segment individual microcalcifications. An outlier labeling and nonlinear subband decomposition based microcalcification segmentation method is also investigated. Two types of lesions, namely mass and stellate lesions, might be indicators of breast cancer. Finally, we propose a snake algorithm based scheme to detect the boundaries of mass lesions on mammograms. This scheme is compared with a recently developed region growing based boundary detection method. It is observed that the snake algorithm results in a more smooth boundary which is consistent with the morphological structure of mass lesions.Gürcan, Metin NafiPh.D

Measuring Chemotherapy Response in Breast Cancer Using Optical and Ultrasound Spectroscopy

Purpose: This study comprises two subprojects. In subproject one, the study purpose was to evaluate response to neoadjuvant chemotherapy (NAC) using quantitative ultrasound (QUS) and diffuse optical spectroscopy imaging (DOS) in locally advanced breast cancer (LABC) during chemotherapy. In subproject two, DOS-based functional maps were analysed with texture-based image features to predict breast cancer response before the start of NAC. Patients and Measurements: The institution’s ethics review board approved this study. For subproject one, subjects (n=22) gave written consent before participating in the study. Participants underwent non-invasive, DOS and QUS imaging. Data were acquired at weeks 0 (i.e. baseline), 1, 4, 8 and before surgical removal of the tumour (mastectomy and/or lumpectomy); corresponding to chemotherapy schedules. QUS parameters including the midband fit (MBF), 0-MHz intercept (SI), and the spectral slope (SS) were determined from tumour ultrasound data using spectral analysis. In the same patients, DOS was used to measure parameters relating to tumour haemoglobin and tissue composition such as %Water and %Lipids. Discriminant analysis and receiver-operating characteristic (ROC) analyses were used to correlate the measured imaging parameters to Miller-Payne pathological response during treatment. Additionally, multivariate analysis was carried out for pairwise DOS and QUS parameter combinations to determine if an increase in the classification accuracy could be obtained using combination DOS and QUS parametric models. For subproject two, 15 additional patients we recruited after first giving their written informed consent. A pooled analysis was completed for all DOS baseline data (subproject 1 and subproject 2; n=37 patients). LABC patients planned for NAC had functional DOS maps and associated textural features generated. A grey-level co-occurrence matrix (texture) analysis was completed for parameters associated with haemoglobin, tissue composition, and optical properties (deoxy-haemoglobin [Hb], oxy-haemoglobin [HbO2], total haemoglobin [HbT]), %Lipids, %Water, and scattering power [SP], scattering amplitude [SA]) prior to treatment. Textural features included contrast (con), vi correlation (cor), energy (ene), and homogeneity (hom). Patients were classified as ‘responders’ or ‘non-responders’ using Miller-Payne pathological response criteria after treatment completion. In order to test if baseline univariate texture features could predict treatment response, a receiver operating characteristic (ROC) analysis was performed, and the optimal sensitivity, specificity and area under the curve (AUC) was calculated using Youden’s index (Q-point) from the ROC. Multivariate analysis was conducted to test 40 DOS-texture features and all possible bivariate combinations using a naïve Bayes model, and k-nearest neighbour (k-NN) model classifiers were included in the analysis. Using these machine-learning algorithms, the pretreatment DOS-texture parameters underwent dataset training, testing, and validation and ROC analysis were performed to find the maximum sensitivity and specificity of bivariate DOS-texture features. Results: For subproject one, individual DOS and QUS parameters, including the spectral intercept (SI), oxy-haemoglobin (HbO2), and total haemoglobin (HbT) were significant markers for response outcome after one week of treatment (p<0.01). Multivariate (pairwise) combinations increased the sensitivity, specificity and AUC at this time; the SI+HbO2 showed a sensitivity/specificity of 100%, and an AUC of 1.0 after one week of treatment. For subproject two, the results indicated that textural characteristics of pre-treatment DOS parametric maps can differentiate treatment response outcomes. The HbO2-homogeneity resulted in the highest accuracy amongst univariate parameters in predicting response to chemotherapy: sensitivity (%Sn) and specificity (%Sp) = 86.5 and 89.0%, respectively and an accuracy of 87.8%. The highest predictors using multivariate (binary) combination features were the Hb-Contrast + HbO2-Homogeneity which resulted in a %Sn = 78.0, a %Sp = 81.0% and an accuracy of 79.5% using the naïve Bayes model. Conclusion: DOS and QUS demonstrated potential as coincident markers for treatment response and may potentially facilitate response-guided therapies. Also, the results of this study demonstrated that DOS-texture analysis can be used to predict breast cancer response groups prior to starting NAC using baseline DOS measurements

Histopathological image analysis with connections to genomics

The fields of imaging and genomics in cancer research have been mostly studied independently, but recently available datasets have made investigation into the synergy of these two fields possible. This work demonstrates the efficacy of computational histopathological image analysis to extract meaningful quantitative nuclear and cellular features from hematoxylin and eosin stained images that have meaningful connections to genomic data. Additionally, with the advent of whole slide images, significantly more data representing the variation in nuclear characteristics and tumor heterogeneity is available, which can aid in developing new analytical tools, such as the proposed convolutional neural network for nuclear segmentation, which produces state-of-the-art segmentation results on challenging cases seen in normal pathology. This robust segmentation tool is essential for capturing reliable features for computational pathology. Additionally, whole slide images capture rich spatial information about tumors, which presents a challenge, but also an opportunity for the development of new image processing tools to capture this spatial information, which could be considered for future work. Other histopathological image modalities and relevant machine learning tools are also considered for elucidating cellular processes of cancer

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 192

This bibliography lists 247 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1979

Multiparametric monitoring of chemotherapy treatment response in locally advanced breast cancer using quantitative ultrasound and diffuse optical spectroscopy

Purpose: This study evaluated pathological response to neoadjuvant chemotherapy using quantitative ultrasound (QUS) and diffuse optical spectroscopy imaging (DOSI) biomarkers in locally advanced breast cancer (LABC). Materials and Methods: The institution’s ethics review board approved this study. Subjects (n = 22) gave written informed consent prior to participating. US and DOSI data were acquired, relative to the start of neoadjuvant chemotherapy,at weeks 0, 1, 4, 8 and preoperatively. QUS parameters including the mid-band fit (MBF), 0-MHz intercept (SI), and the spectral slope (SS) were determined from tumor ultrasound data using spectral analysis. In the same patients, DOSI was used to measure parameters relating to tumor hemoglobin and composition. Discriminant analysis and receiver-operating characteristic (ROC) analysis was used to classify clinical and pathological response during treatment and to estimate the area under the curve (AUC). Additionally, multivariate analysis was carried out for pairwise QUS/DOSI parameter combinations using a logistic regression model. Results: Individual QUS and DOSI parameters, including the (SI), oxy-haemoglobin (HbO2), and total hemoglobin (HbT) were significant markers for response after one week of treatment (p < 0.01). Multivariate (pairwise) combinations increased the sensitivity, specificity and AUC at this time; the SI + HbO2 showed a sensitivity/ specificity of 100%, and an AUC of 1.0. Conclusions: QUS and DOSI demonstrated potential as coincident markers for treatment response and may potentially facilitate response-guided therapies. Multivariate QUS and DOSI parameters increased the sensitivity and specificity of classifying LABC patients as early as one week after treatment

Neuro-imagerie multimodale et multirésolution de cerveaux de souris combinant l’histologie sérielle par tomographie en cohérence optique et l’IRM de diffusion

L’histologie sérielle est une technique d’imagerie permettant d’observer des échantillons entiers à haute résolution. Cette technique consiste à trancher de fines couches de tissu, puis à déplacer l’échantillon sous un objectif de microscope afin d’acquérir autant d’images que nécessaire pour couvrir toute la surface révélée par la coupe. Ce processus est automatisé et est répété jusqu’à ce que tout l’échantillon soit imagé, c’est-à-dire un cerveau de souris dans cette thèse. Couplée à un microscope par tomographie en cohérence optique (OCT), cette modalité est capable de cartographier la distribution spatiale de la matière blanche dans des cerveaux entiers de souris. L’objectif principal de cette thèse était de développer les méthodes de reconstruction nécessaires à l’assemblage en un seul volume des milliers d’images acquises par un système d’histologie massive. De plus, dans cette première phase du projet, des méthodes permettant d’aligner les données sur des images IRM acquises pour les mêmes animaux ont été développées. Cela a permis de mieux comprendre l’origine du contraste optique dans le cerveau et cela offre maintenant la possibilité d’intégrer l’histologie massive dans les études de neuro-imagerie employant des groupes d’animaux. Dans une seconde phase du projet, un microscope à cohérence optique haute résolution a été ajouté au système d’histologie par OCT existant. Cette nouvelle plateforme d’imagerie utilise les images à basse résolution comme repère pour localiser au sein du cerveau les images à haute résolution du second microscope. L’utilité d’une telle plateforme réside dans le fait qu’il est maintenant possible de cibler des régions spécifiques à observer en détail sans avoir à imager un cerveau entier à cette grande résolution, ce qui représenterait plusieurs semaines de mesurage et des quantités immenses de données à assembler. Les données mesurées avec la nouvelle plateforme ont été intégrées à la procédure de reconstruction et d’alignement développé pour la première phase du projet. Ainsi, il a été possible de comparer les images à grande résolution avec les données d’IRM de diffusion acquises pour les mêmes cerveaux de souris. Ceci a permis de confirmer des hypothèses posées lors de l’analyse des données IRM de diffusion à partir de la microscopie. Les méthodes de reconstruction, d’alignement et d’analyse développées, ainsi que la nouvelle plateforme d’histologie sérielle bi-résolution par OCT, offrent enfin la possibilité d’utiliser cette modalité optique pour réaliser des études de groupes animales ou bien pour valider des mesures faites dans le cerveau avec d’autres modalités d’imagerie telle que l’IRM de diffusion.----------ABSTRACT Serial histology is an imaging technique able to observe whole samples at high resolution. This technique involves cutting thin tissue layers, followed by the positioning of the sample under a microscope objective and the acquisition of as many images as necessary to cover the entire area revealed by the cut. This process is automated and is repeated until the entire brain has been imaged. Coupled with an optical coherence tomography (OCT) microscope, this modality is able to map the spatial distribution of white matter in whole mouse brains. The main objective of this thesis was to develop the reconstruction methods necessary for the assembly into a single volume of the thousands of images acquired with a massive histology system. In addition, in this first project phase, methods for aligning data on MRI images acquired for the same animals have been developed. This has led to a better understanding of the optical contrast origin in the brain and it now offers the possibility of integrating massive histology into neuroimaging studies using animal groups. In a second phase of the project, a high resolution optical coherence microscope was added to the existing OCT histology system. This new imaging platform uses low-resolution images as a reference to locate the high-resolution images of the second microscope within the brain. The usefulness of such a platform lies in the fact that it is now possible to target specific regions to observe in detail without having to image an entire brain at this high resolution, which would represent several weeks for measurements and immense quantities of data to assemble. The data measured with the new platform have been incorporated into the reconstruction and alignment procedure developed for the first phase of the project. Thus, it was possible to compare the high resolution images with the diffusion MRI data acquired for the same mouse brains. This made it possible to confirm hypotheses posed during the analysis of diffusion MRI data. The methods of reconstruction, alignment and analysis developed during this thesis, as well as the new dual resolution serial OCT histology platform, finally offer the possibility of using this optical modality to carry out studies of animal groups or to validate measurements made in a brain with other imaging modalities such as diffusion MRI