Autofocusing for automated microscopic evaluation of blood smear and pap smear

A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification

Cervical cancer is the one of the most common cancers in women worldwide, affecting around 570,000 new patients each year. Although there have been great improvements over the years, current screening procedures can still suffer from long and tedious workflows and ambiguities. The increasing interest in the development of computer-aided solutions for cervical cancer screening is to aid with these common practical difficulties, which are especially frequent in the low-income countries where most deaths caused by cervical cancer occur. In this review, an overview of the disease and its current screening procedures is firstly introduced. Furthermore, an in-depth analysis of the most relevant computational methods available on the literature for cervical cells analysis is presented. Particularly, this work focuses on topics related to automated quality assessment, segmentation and classification, including an extensive literature review and respective critical discussion. Since the major goal of this timely review is to support the development of new automated tools that can facilitate cervical screening procedures, this work also provides some considerations regarding the next generation of computer-aided diagnosis systems and future research directions.

Region-based multifocus image fusion for the precise acquisition of Pap smear images

A multifocus image fusion method to obtain a single focused image from a sequence of microscopic high-magnification Papanicolau source (Pap smear) images is presented. These images, captured each in a different position of the microscope lens, frequently show partially focused cells or parts of cells, which makes them unpractical for the direct application of image analysis techniques. The proposed method obtains a focused image with a high preservation of original pixels information while achieving a negligible visibility of the fusion artifacts. The method starts by identifying the best-focused image of the sequence; then, it performs a mean-shift segmentation over this image; the focus level of the segmented regions is evaluated in all the images of the sequence, and best-focused regions are merged in a single combined image; finally, this image is processed with an adaptive artifact removal process. The combination of a region-oriented approach, instead of block-based approaches, and a minimum modification of the value of focused pixels in the original images achieve a highly contrasted image with no visible artifacts, which makes this method especially convenient for the medical imaging domain. The proposed method is compared with several state-of-the-art alternatives over a representative dataset. The experimental results show that our proposal obtains the best and more stable quality indicators.

Retrospective non-uniform illumination correction techniques in images of tuberculosis

Image pre-processing is highly significant in automated analysis of microscopy images. In this work, non-uniform illumination correction has been attempted using the surface fitting method (SFM), multiple regression method (MRM), and bidirectional empirical mode decomposition (BEMD) in digital microscopy images of tuberculosis (TB). The sputum smear positive and negative images recorded under a standard image acquisition protocol were subjected to illumination correction techniques and evaluated by error and statistical measures. Results show that SFM performs more efficiently than MRM or BEMD. The SFM produced sharp images of TB bacilli with better contrast. To further validate the results, multifractal analysis was performed that showed distinct variation before and after implementation of illumination correction by SFM. Results demonstrate that after illumination correction, there is a 26% increase in the number of bacilli, which aids in classification of the TB images into positive and negative, as TB positivity depends on the count of bacilli.

Automatic section thickness determination using an absolute gradient focus function

Quantitative analysis of microstructures using computerized stereology systems is an essential tool in many disciplines of bioscience research. Section thickness determination in current nonautomated approaches requires manual location of upper and lower surfaces of tissue sections. In contrast to conventional autofocus functions that locate the optimally focused optical plane using the global maximum on a focus curve, this study identified by two sharp 'knees' on the focus curve as the transition from unfocused to focused optical planes. Analysis of 14 grey-scale focus functions showed, the thresholded absolute gradient function, was best for finding detectable bends that closely correspond to the bounding optical planes at the upper and lower tissue surfaces. Modifications to this function generated four novel functions that outperformed the original. The 'modified absolute gradient count' function outperformed all others with an average error of 0.56 μm on a test set of images similar to the training set; and, an average error of 0.39 μm on a test set comprised of images captured from a different case, that is, different staining methods on a different brain region from a different subject rat. We describe a novel algorithm that allows for automatic section thickness determination based on just out-of-focus planes, a prerequisite for fully automatic computerized stereology.