A Portable System for Screening of Cervical Cancer

Cervical cancer is one of the most common cancers that affect women, with the highest incidence and mortality rates occurring in low- and middle-income countries. Early detection is crucial for successful treatment, but the need for expensive equipment, trained colposcopists, and clinical infrastructure has made it difficult to eradicate this disease. Accurately determining the size and location of a precancerous lesion involves specialized and costly equipment, making it difficult to track the progression of the disease or the efficacy of treatment. Imaging and machine learning techniques have been attempted by several researchers to overcome these limitations, but the subjective nature of diagnosis and other challenges persist. Therefore, there is a need to develop a system to automatically segment lesions on the cervix and quantify their size in relation to the cervical region of interest. Challenges to the automated detection of cervical cancer include:• Low quality of the devices used, which impair the image resolution; lighting conditions, which can make shadows appear, hindering the ability to find the cervical region of interest (ROI); distortion of the images due to the presence of glare or specular reflections (SR) from the light source; and the appearance of artifacts such as the speculum and surrounding tissue. The limitations that exist in selecting or designing a device to acquire cervical images (cervigrams) have been investigated. • The acquisition of cervical images requires access to sensitive patient information, which raises concerns about patient privacy and data security. Ensuring that patient data is protected and used only for diagnostic purposes is critical to building patient trust and ensuring widespread adoption of automated screening technologies. A pilot study to capture cervigrams from women that present early signs of cervical cancer was designed. Relevant data would be collected to further understand the progression of this disease, while maintaining privacy and confidentiality of the participants in the study. • The early detection of cervical cancer requires analyzing complex data, including images, pathology reports, and medical records. Automating the analysis of this data requires machine learning algorithms or image processing techniques capable of interpreting such information. Image processing methods based on traditional and machine learning techniques were leveraged to identify the cervical region of interest and remove light reflections from the cervical epithelium. Lesions present on the cervix were detected and their size, invariant with respect to the orientation of the camera or its distance from the cervix, was calculated. • Finally, variability and subjectivity are involved when acquiring and analyzing cervigrams. A graphical user interface was developed to facilitate data collection and analysis throughout the pilot study and future clinical trials. Results indicate that it is possible to segment images of the cervix, reduce the effect of glare from light sources, remove specular reflections and other artifacts, and successfully detect and quantify lesions through the proposed methods. The above approaches are demonstrated throughout this dissertation to show that a low-cost bioinformatics-based tool for early detection of cervical cancer can be achieved for screening patients in a clinical setting. While the algorithms used for analysis were validated using sample images from public databases, it is crucial to conduct small-scale clinical trials to further validate these methods. Furthermore, the use of more advanced image processing techniques or machine learning algorithms to improve the accuracy and speed of lesion detection is under review

Labor Induction failure prediction based on B-Mode Ultrasound Image Processing using Multiscale Local Binary Patterns

Labor induction is defined as the artificial onset of labor for the purpose of vaginal birth. Cesarean section is one of the potential risks of labor induction as it occurs in about 20% of the inductions. A ripe cervix (soft and distensible) is needed for a successful labor. Changes occurring during the ripening process, will affect the interaction between cervical tissues and sound waves during ultrasound transvaginal scanning and will be perceived as gray level intensity variations in the echography image. Thus, a non-invasive method using image processing of ultrasound images may help in predicting the outcome of labor induction

Emerging Technologies, Signal Processing and Statistical Methods for Screening of Cervical Cancer In Vivo: Are They Good Candidates for Cervical Screening?

The current cervical cancer screening test (the Pap smear) is a manual cytological procedure. This cytology test has various limitations and many errors. Excellent candidates for improving the performance of the cervical cancer screening procedure are electro-optical systems (EOSs), used for assessment of the cervical cancer precursors in vivo, such as digital spectroscopy, digital colposcopy and bioelectrical phenomena-based systems. These EOSs use the advantages of signal processing methods and can replace the qualitative assessments, with objective metrics. The EOSs can be used as an adjunct to the current screener or as a primary screener. We analyse and discuss the effectiveness of the signal processing and statistical methods for diagnosis of cervical cancer in vivo. This analysis is reinforced by the presentation of the scientific and clinical contributions of these methods in clinical practice. As a result of this analysis, we outline and discuss the well-established estimates of the signal processing features and the ambiguous features, that are used for classification of the cervical pre-cancer in vivo

A Polarization-Imaging-Based Machine Learning Framework for Quantitative Pathological Diagnosis of Cervical Precancerous Lesions

Polarization images encode high resolution microstructural information even at low resolution. We propose a framework combining polarization imaging and traditional microscopy imaging, constructing a dual-modality machine learning framework that is not only accurate but also generalizable and interpretable. We demonstrate the viability of our proposed framework using the cervical intraepithelial neoplasia grading task, providing a polarimetry feature parameter to quantitatively characterize microstructural variations with lesion progression in hematoxylin-eosin-stained pathological sections of cervical precancerous tissues. By taking advantages of polarization imaging techniques and machine learning methods, the model enables interpretable and quantitative diagnosis of cervical precancerous lesion cases with improved sensitivity and accuracy in a low-resolution and wide-field system. The proposed framework applies routine image-analysis technology to identify the macro-structure and segment the target region in H&E-stained pathological images, and then employs emerging polarization method to extract the micro-structure information of the target region, which intends to expand the boundary of the current image-heavy digital pathology, bringing new possibilities for quantitative medical diagnosis