Near real-time classification of optical coherence tomography data using principal components fed linear discriminant analysis

Three-dimensional dynamics optical coherence tomography for tumor spheroid evaluation

We present a completely label-free three-dimensional (3D) optical coherence tomography (OCT)-based tissue dynamics imaging method for visualization and quantification of the metabolic and necrotic activities of tumor spheroid. Our method is based on a custom 3D scanning protocol that is designed to capture volumetric tissue dynamics tomography images only in a few tens of seconds. The method was applied to the evaluation of a tumor spheroid. The time-course viability alteration and anti-cancer drug response of the spheroid were visualized qualitatively and analyzed quantitatively. The similarity between the OCT-based dynamics images and fluorescence microscope images was also demonstrated.

Speckle statistics in OCT images: Monte Carlo simulations and experimental studies

The speckle pattern of an optical coherence tomography (OCT) image carries potentially useful sample information that may assist in tissue characterization. Recent biomedical results in vivo indicate that the distribution of signal intensities within an OCT tissue image is well described by a log-normal-like (Gamma) function. To fully understand and exploit this finding, an OCT Monte Carlo model that accounts for speckle effects was developed. The resultant Monte Carlo speckle statistics predictions agree well with experimental OCT results from a series of control phantoms with variable scattering properties; the Gamma distribution provides a good fit to the theoretical and experimental results. The ability to quantify subresolution tissue features via OCT speckle analysis may prove useful in diagnostic photomedicine.

Criteria for pathology recognition in optical coherence tomography of fallopian tubes

An increase of infertility and chronic pelvic pains syndrome, a growing level of latent diseases of this group, as well as a stably high percentage (up to 25% for infertility and up to 60% for the chronic pelvic pains syndrome) of undetermined origin raises the requirement for novel introscopic diagnostic techniques. We demonstrate abilities of optical coherence tomography (OCT) as a complementary technique to laparoscopy in diagnostics of fallopian tubes pathologies. We have acquired OCT images of different parts of fallopian tubes in norm and with morphologically proven pathology. Based on comparative analysis of the OCT data and the results of histological studies, we have worked out the subjective OCT criteria for distinguishing between unaltered and pathologic tissues. The developed criteria are verified in blind recognition tests. Diagnostic efficacy of OCT diagnostics in the case ofpelvic inflammatory diseases has been statistically evaluated, and high diagnostic accuracy (88%) is shown. Basing of the subjective criteria, an attempt to develop independent criteria aimed for automated recognition of pathological states in fallopian tubes is undertaken. Enhanced diagnostic accuracy (96%) of the developed independent criteria is demonstrated.

Morphological analysis of optical coherence tomography images for automated classification of gastrointestinal tissues

The impact of digestive diseases, which include disorders affecting the oropharynx and alimentary canal, ranges from the inconvenience of a transient diarrhoea to dreaded conditions such as pancreatic cancer, which are usually fatal. Currently, the major limitation for the diagnosis of such diseases is sampling error because, even in the cases of rigorous adherence to biopsy protocols, only a tiny fraction of the surface of the involved gastrointestinal tract is sampled. Optical coherence tomography (OCT), which is an interferometric imaging technique for the minimally invasive measurement of biological samples, could decrease sampling error, increase yield, and even eliminate the need for tissue sampling provided that an automated, quick and reproducible tissue classification system is developed. Segmentation and quantification of ophthalmologic pathologies using OCT traditionally rely on the extraction of thickness and size measures from the OCT images, but layers are often not observed in nonopthalmic OCT imaging. Distinct mathematical methods, namely Principal Component Analysis (PCA) and textural analyses including both spatial textural analysis derived from the two-dimensional discrete Fourier transform (DFT) and statistical texture analysis obtained independently from center-symmetric autocorrelation (CSAC) and spatial grey-level dependency matrices (SGLDM), have been previously reported to overcome this problem. We propose an alternative approach consisting of a region segmentation according to the intensity variation along the vertical axis and a pure statistical technique for feature quantification, i.e. morphological analysis. Qualitative and quantitative comparisons with traditional approaches are accomplished in the discrimination of freshly-excised specimens of gastrointestinal tissues to exhibit the feasibility of the proposed method for computer-aided diagnosis (CAD) in the clinical setting.

Scatterer size-based analysis of optical coherence tomography images using spectral estimation techniques

A novel spectral analysis technique of OCT images is demonstrated in this paper for classification and scatterer size estimation. It is based on SOCT autoregressive spectral estimation techniques and statistical analysis. Two different statistical analysis methods were applied to OCT images acquired from tissue phantoms, the first method required prior information on the sample for variance analysis of the spectral content. The second method used k-means clustering without prior information for the sample. The results are very encouraging and indicate that the spectral content of OCT signals can be used to estimate scatterer size and to classify dissimilar areas in phantoms and tissues with sensitivity and specificity of more than 90%.

Method for optical coherence tomography image classification using local features and earth mover's distance

Optical coherence tomography (OCT) is a recent imaging method that allows high-resolution, cross-sectional imaging through tissues and materials. Over the past 18 years, OCT has been successfully used in disease diagnosis, biomedical research, material evaluation, and many other domains. As OCT is a recent imaging method, until now surgeons have limited experience using it. In addition, the number of images obtained from the imaging device is too large, so we need an automated method to analyze them. We propose a novel method for automated classification of OCT images based on local features and earth mover's distance (EMD). We evaluated our algorithm using an OCT image set which contains two kinds of skin images, normal skin and nevus flammeus. Experimental results demonstrate the effectiveness of our method, which achieved classification accuracy of 0.97 for an EMD+KNN scheme and 0.99 for an EMD+SVM (support vector machine) scheme, much higher than the previous method. Our approach is especially suitable for nonhomogeneous images and could be applied to a wide range of OCT images.

Review: Optical Micrometer Resolution Scanning for Non-invasive Grading of Precancer in the Human Uterine Cervix

Management of cervical precancer is archetypal for other cancer prevention programmes but has to consider diagnostic and logistic challenges. Numerous optical tools are emerging for non-destructive near real-time early diagnosis of precancerous lesions of the cervix. Non-destructive, real-time imaging modalities have reached pre-commercial status, but high resolution mapping tools are not yet introduced in clinical settings. The NCBI PubMed web page was searched using the keywords ‘CIN diagnosis’ and the combinations of ‘cervix {confocal, optical coherence tomography, ftir, infrared, Raman, vibrational, spectroscopy}’. Suitable titles were identified and their relevant references followed. Challenges in precancer management are discussed. The following tools capable of non-destructive high resolution mapping in a clinical environment were selected: confocal microscopy, optical coherence tomography, IR spectroscopy, and Raman spectroscopy. Findings on the clinical performance of these techniques are put into context in order to assist the reader in judging the likely performance of these methods as diagnostic tools. Rationale for carrying out research under the prospect of the HPV vaccine is given.

Towards automated classification of clinical optical coherence tomography data of dense tissues

The native contrast of optical coherence tomography (OCT) data in dense tissues can pose a challenge for clinical decision making. Automated data evaluation is one way of enhancing the clinical utility of measurements. Methods for extracting information from structural OCT data are appraised here. A-scan analysis allows characterization of layer thickness and scattering parameters, whereas image analysis renders itself to segmentation, texture and speckle analysis. All fully automated approaches combine pre-processing, feature registration, data reduction, and classification. Pre-processing requires de-noising, feature recognition, normalization and refining. In the current literature, image exclusion criteria, initial parameters, or manual input are common requirements. The interest of the presented methods lies in the prospect of objective, quick, and/or post-acquisition processing. There is a potential to improve clinical decision making based on automated processing of OCT data.

Optical coherence tomography: a potential tool for unsupervised prediction of treatment response for Port-Wine Stains

BACKGROUND

Treatment of Port-Wine Stains (PWS) suffers from the absence of a reliable real-time tool for monitoring a clinical endpoint. Response to treatment varies substantially according to blood vessel geometry. Even though optical coherence tomography (OCT) has been identified as a modality with potential to suit this need, it has not been introduced as a standard clinical monitoring tool. One reason could be that - although OCT acquires data in real-time - gigabyte data transfer, processing and communication to a clinician may impede the implementation as a clinical tool.

OBJECTIVES

We investigate whether an automated algorithm can address this problem.

METHODS

Based on our understanding of pulsed dye laser treatment, we present the implementation of an unsupervised, real-time classification algorithm which uses principal components data reduction and linear discriminant analysis. We evaluate the algorithm using 96 synthesized test images and 7 clinical images.

RESULTS

The synthesized images are classified correctly in 99.8%. The clinical images are classified correctly in 71.4%.

CONCLUSIONS

Principal components-fed linear discriminant analysis (PC-fed LDA) may be a valuable method to classify clinical images. Larger sampling numbers are required for a better training model. These results justify undertaking a study involving more patients and show that disease can be described as a function of available treatment options.