| dc.description.abstract | Photoacoustic imaging is a noninvasive imaging modality which combines the bene ts
of optical contrast and ultrasonic resolution. It is applied widely for monitoring tissue
health conditions in the elds of cardiology, ophthalmology, oncology, dermatology, and
neurosciences. The photoacoustic tomographic image reconstruction problem is typically
ill-posed and requires model-based iterative algorithms. The microscopic image
analysis of pathological slides is considered as a gold standard for medical diagnosis. To
acquire good quality images, one needs to deploy high-cost microscopes, which becomes
prohibitive to have utility low-resource settings. The low-cost microscopic image have
low quality due to its inability to acquire focused stack. The thesis deploys methods
based on vector extrapolation and guided ltering to improve these photoacoustic and
histopathology (microscopic) images.
The limited data photoacoustic tomographic image reconstruction problem is known
to be ill-posed and hence the iterative reconstruction methods were proven to be effective
in terms of providing good quality initial pressure distribution. Often, these iterative
methods require a large number of iterations to converge to a solution, in turn making
the image reconstruction procedure computationally inefficient. Two variants of vector
polynomial extrapolation techniques were proposed to accelerate two standard iterative
photoacoustic image reconstruction algorithms, including regularized steepest descent
and total variation regularization methods. It was shown using numerical and experimental
phantom cases that these extrapolation methods that are proposed in this thesis
can provide significant acceleration (as high as 4.7 times) along with added advantage
of improving reconstructed image quality. Several algorithms exist to solve the photoacoustic image reconstruction problem depending
on the expected reconstructed image features. These reconstruction algorithms
promote typically one feature, such as being smooth or sharp, in the output image. Combining
these features using a guided filtering approach was proposed in this thesis, which
requires an input and a guiding image. This approach act as a post processing step to
improve commonly used Tikhonov or total variational regularization method. The result
obtained from linear backprojection was used as a guiding image to improve these results.
Using both numerical and experimental phantom cases, it was shown that the proposed
guided ltering approach was able to improve (as high as 11.23 dB) the signal-to-noise
ratio of the reconstructed images with added advantage being computationally e cient.
This approach was compared with state-of-the-art basis pursuit deconvolution as well as
standard denoising methods and shown to outperform them.
Microscopic analysis of pathological slide smears is the gold standard for medical
diagnosis, therefore the research community is making e orts towards low-cost image
acquisition and automated computational analysis equipment that is especially suitable
for developing countries. However, the requirement of the images being very well
in focus may not be met with these equipment and thus image enhancement methods
that can compensate for this shortcoming gain critical importance. A guided ltering
(GF) based approach was proposed for enhancement of out-of-focus microscopic images
of human blood smear slides containing healthy and malaria infected Red Blood
Cells (h-RBCs and i-RBCs) and PAP smear. Comparisons have also been made with a
histogram-equalization methods for image enhancement (CLAHE), RIQMC-based optimal
histogram matching (ROHIM), modi ed L0 based method and the proposed guided
ltering method has been shown to outperform these methods. The guided ltering enhanced
images lead to better segmentation accuracy and visual quality compared to the
native ones. Both these traits are necessary to perform automated diagnosis via image
processing and machine learning and hence the method proposed in this thesis work can
play an important role towards the goal of universal healthcare.
This thesis work aims at improving the photoacoustic tomography images as well as
histopathological microscopic images, where quality of images is an important factor to
provide correct diagnosis. The thesis work proposed fast and improved post-processing
methods for photoacoustic and microscopic images, especially in cases these images tend
to be noisy. The central theme of this thesis work was to improve the quality of photoacoustic/
microscopic images obtained in limited/low-quality data scenarios. In microscopy,
the low cost apparatus used for obtaining the microscopic images are often
corrupted with noise and provide very limited diagnostic accuracy, especially with automated
algorithms. Various methods were proposed and systematically evaluated for
performing post-processing of the data obtained using these limited data and low quality
data scenarios for photoacoustic and microscopic images. | en_US |
