Journal of Innovative Optical Health Sciences

The concept of region of sensitivity is central to the field of diffuse optics and is closely related to the Jacobian matrix used to solve the inverse problem in imaging. It is well known that, in diffuse reflectance, the region of sensitivity associated with a given source–detector pair is shaped as a banana, and features maximal sensitivity to the portions of the sample that are closest to the source and the detector. We have recently introduced a dual-slope (DS) method based on a special arrangement of two sources and two detectors, which results in deeper and more localized regions of sensitivity, resembling the shapes of different kinds of nuts. Here, we report the regions of sensitivity associated with a variety of source–detector arrangements for DS measurements of intensity and phase with frequency-domain spectroscopy (modulation frequency: 140MHz) in a medium with absorption and reduced scattering coefficients of 0.1 and 12 cm${}^{-1}$, respectively. The main result is that the depth of maximum sensitivity, considering only cases that use source-detector separations of 25 and 35 mm, progressively increases as we consider single-distance intensity (2.0mm), DS intensity (4.6mm), single-distance phase (7.5mm), and DS phase (10.9mm). These results indicate the importance of DS measurements, and even more so of phase measurements, when it is desirable to selectively probe deeper portions of a sample with diffuse optics. This is certainly the case in non-invasive optical studies of brain, muscle, and breast tissue, which are located underneath the superficial tissue at variable depths.

Chemical imaging (CI) possesses a strong ability of pharmaceutical analysis. Its great strength relies on the integration of traditional spectroscopy (one dimension) and imaging technique (two dimensions) to generate three-dimensional data hypercubes. Data pre-processing or processing methods are proposed to analyze vast data matrixes and thereby realizing different research objectives. In this review paper, various pharmaceutical applications of quality control over the past few years are summed up in two groups of final product test and industrial utilization. The scope of “quality control” here includes traditional analytical use, process understanding and manufactural control. Finally, two major challenges about undesirable sample geometry and lengthy acquisition time are discussed for prospective commercial or industrial application.

Early detection of vulnerable plaques is the critical step in the prevention of acute coronary events. Morphology, composition, and mechanical property of a coronary artery have been demonstrated to be the key characteristics for the identification of vulnerable plaques. Several intravascular multimodal imaging technologies providing co-registered simultaneous images have been developed and applied in clinical studies to improve the characterization of atherosclerosis. In this paper, the authors review the present system and probe designs of representative intravascular multimodal techniques. In addition, the scientific innovations, potential limitations, and future directions of these technologies are also discussed.

Optical coherence tomography angiography (OCTA) has emerged as an advanced in vivo imaging modality, which is widely used for the clinic ophthalmology and neuroscience research in the rodent brain cortex among others. Based on the high numerical aperture (NA) probing lens and the motion-corrected algorithms, a high-resolution imaging technique called OCT micro-angiography is applied to resolve the small blood capillary vessels ranging from 5$\mu$m to 10$\mu$m in diameter. As OCT-based techniques are recently evolving further from the structural imaging of capillaries toward spatio-temporal dynamic imaging of blood flow in capillaries, here we present a review on the latest techniques for the dynamic flow imaging. Studies on capillary blood flow using these techniques will help us better understand the roles of capillary blood flow for normal functioning of the brain as well as how it malfunctions in diseases.

We introduce a method based on Gaussian mixture model (GMM) clustering and level-set to automatically detect intraretina fluid on diabetic retinopathy (DR) from spectral domain optical coherence tomography (SD-OCT) images in this paper. First, each B-scan is segmented using GMM clustering. The original clustering results are refined using location and thickness information. Then, the spatial information among every consecutive five B-scans is used to search potential fluid. Finally, the improved level-set method is used to obtain the accurate boundaries. The high sensitivity and accuracy demonstrated here show its potential for detection of fluid.

This paper attempts to estimate diagnostically relevant measure, i.e., Arteriovenous Ratio with an improved retinal vessel classification using feature ranking strategies and multiple classifiers decision-combination scheme. The features exploited for retinal vessel characterization are based on statistical measures of histogram, different filter responses of images and local gradient information. The feature selection process is based on two feature ranking approaches (Pearson Correlation Coefficient technique and Relief-F method) to rank the features followed by use of maximum classification accuracy of three supervised classifiers (k-Nearest Neighbor, Support Vector Machine and Naïve Bayes) as a threshold for feature subset selection. Retinal vessels are labeled using the selected feature subset and proposed hybrid classification scheme, i.e., decision fusion of multiple classifiers. The comparative analysis shows an increase in vessel classification accuracy as well as Arteriovenous Ratio calculation performance. The system is tested on three databases, a local dataset of 44 images and two publically available databases, INSPIRE-AVR containing 40 images and VICAVR containing 58 images. The local database also contains images with pathologically diseased structures. The performance of the proposed system is assessed by comparing the experimental results with the gold standard estimations as well as with the results of previous methodologies. Overall, an accuracy of 90.45%, 93.90% and 87.82% is achieved in retinal blood vessel separation with 0.0565, 0.0650 and 0.0849 mean error in Arteriovenous Ratio calculation for Local, INSPIRE-AVR and VICAVR dataset, respectively.

Computer-assisted cervical screening is an effective method to save the doctors’ workload and improve their work efficiency. Usually, the correct classification of cervical cells depends on the nuclear segmentation effect and the extraction of nuclear features. However, the precise nucleus segmentation remains a huge challenge, especially for densely distributed nucleus. Moreover, previous cellular classification methods are mostly based on morphological features of nucleus size or color. Those individual features can make accurate classification for severe lesions, but not for mild lesions. In this paper, we propose an accurate instance segmentation algorithm and propose cognition-based features to identify cervical cancer cells. Different from previous individual nucleus features, we also propose population features and cognition-based features according to the Bethesda System (TBS) for reporting cervical cytology and the diagnostic experience of the cytologists. The results showed that the segmentation achieves better success in complex situations than that by traditional segmentation algorithms. Besides, the cell classification via cognition-based features also help us find out more about less severe lesions’ nuclei than that based on conventional features of individual nucleus, meaning an improvement of classification accuracy for cervical screening.

A distinguishing characteristic of normal and cancer cells is the difference in their nuclear chromatin content and distribution. This difference can be revealed by the transmission spectra of nuclei stained with a pH-sensitive stain. Here, we used hematoxylin–eosin (HE) to stain hepatic carcinoma tissues and obtained spectral–spatial data from their nuclei using hyperspectral microscopy. The transmission spectra of the nuclei were then used to train a support vector machine (SVM) model for cell classification. Especially, we found that the chromatin distribution in cancer cells is more uniform, because of which the correlation coefficients for the spectra at different points in their nuclei are higher. Consequently, we exploited this feature to improve the SVM model. The sensitivity and specificity for the identification of cancer cells could be increased to 99% and 98%, respectively. We also designed an image-processing method for the extraction of information from cell nuclei to automate the identification process.

Accurate placement of pedicle screw (PS) is crucial in spinal surgery. Developing new real-time intra-operative monitoring and navigation methods is an important direction of clinical application research. In this paper, we studied the spectrum along the fixation trajectory of PS in frequency domain to tackle the accuracy problem. Fresh porcine vertebrae, bovine vertebrae and ovine vertebrae were measured with the near-infrared spectrum (NIR) device to obtain the reflected spectrum from the vertebrae. Along the fixation trajectory of PS, average energy from different groups was calculated and used for identifying different tissues and compared to achieve the optimal recognition factor. Compared with the time domain approach, the frequency domain method could divide the spectra measured at different tissue points into different groups more stably and accurately, which could serve as a new method to assist the PS insertion. The results gained from this study are significant to the development of hi-tech medical instruments with independent intellectual property rights.