Optical coherence tomography (OCT) is well-known for its high-resolution, non-invasive imaging modality with many medical uses, including skin imaging. Nevertheless, speckle noise limits the analytical capabilities of this imaging tool, causing deterioration in contrast and less exact detection of tissue microstructural heterogeneity. To address this issue, we proposed OCT despeckling approach by combing it with normalization to reduce the speckle noise more effectively. The proposed method contains multiple steps including phase correlation for alignment of misaligned frames, frame averaging which minimizes speckle noise, region-wise pixels normalization that helps to normalize intensity pixels, a modified BM3D filtering to suppress the white and speckle, and contrast enhancement to improve the contrast appropriately. To establish the approach, we applied 130 distinct B-scan skin OCT images and validate and evaluate the performance using qualitatively and quantitatively. Although the output obtained by the algorithm is promising, the method is time-consuming because of a series of steps. To reduce the time complexity, we also develop a supervised deep learning model by mapping between noisy-despeckled image pairs. The effectiveness and applicability of our DL approach were assessed using 130 skin OCT B-scans from various body areas taken from 45 healthy people between the ages of 20 and 60. With the support of the experimental results, we demonstrate that our DL model is capable to normalize and despeckling OCT images simultaneously.
Three-dimensional (3D) photoacoustic tomography is a method of choice for imaging round organs such as brain and breast. Many research groups have used a fully populated hemispherical transducer array with 256, 512, 1028, or 2048 elements and used that for 3D imaging. These transducer arrays are expensive and require a sophisticated data acquisition unit. Other groups have used much smaller number of transducers with a rotating mechanism which eventually filled out the entire hemisphere. We have built a 3D hemispherical array with 28 transducers which are placed on a 3D printed dome-like unit. The location of transducers however may be off-placed by a few millimeters (due to human error and errors in 3D printing). This may be to defocus the reconstructed image if the acceptable positions of transducers are not selected. In this work, we developed a compensation algorithm for misplacement of these transducers using Cuckoo search (CS) algorithm. The CS algorithm finds the optimum location for the transducers using levy flight which relies on levy distribution. The optimum location of each of these transducers is found within -4 mm to 4 mm of their locations. Universal back projection algorithm was used for image reconstruction and the sharpness of 3D image was used as the cost function; additionally, two more objective functions, the Brenner gradient, and the Tenenbaum gradient was investigated.
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