A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms.

Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper's main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic

Data-Driven Deep Learning-Based Analysis on THz Imaging

Breast cancer affects about 12.5% of women population in the United States. Surgical operations are often needed post diagnosis. Breast conserving surgery can help remove malignant tumors while maximizing the remaining healthy tissues. Due to lacking effective real-time tumor analysis tools and a unified operation standard, re-excision rate could be higher than 30% among breast conserving surgery patients. This results in significant physical, physiological, and financial burdens to those patients. This work designs deep learning-based segmentation algorithms that detect tissue type in excised tissues using pulsed THz technology. This work evaluates the algorithms for tissue type classification task among freshly excised tumor samples. Freshly excised tumor samples are more challenging than formalin-fixed, paraffin-embedded (FFPE) block sample counterparts due to excessive fluid, image registration difficulties, and lacking trustworthy pixelwise labels of each tissue sample. Additionally, evaluating freshly excised tumor samples has profound meaning of potentially applying pulsed THz scan technology to breast conserving cancer surgery in operating room. Recently, deep learning techniques have been heavily researched since GPU based computation power becomes economical and stronger. This dissertation revisits breast cancer tissue segmentation related problems using pulsed terahertz wave scan technique among murine samples and applies recent deep learning frameworks to enhance the performance in various tasks. This study first performs pixelwise classification on terahertz scans with CNN-based neural networks and time-frequency based feature tensors using wavelet transformation. This study then explores the neural network based semantic segmentation strategy performing on terahertz scans considering spatial information and incorporating noisy label handling with label correction techniques. Additionally, this study performs resolution restoration for visual enhancement on terahertz scans using an unsupervised, generative image-to-image translation methodology. This work also proposes a novel data processing pipeline that trains a semantic segmentation network using only neural generated synthetic terahertz scans. The performance is evaluated using various evaluation metrics among different tasks

Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further

Novel Computer-Aided Diagnosis Schemes for Radiological Image Analysis

The computer-aided diagnosis (CAD) scheme is a powerful tool in assisting clinicians (e.g., radiologists) to interpret medical images more accurately and efficiently. In developing high-performing CAD schemes, classic machine learning (ML) and deep learning (DL) algorithms play an essential role because of their advantages in capturing meaningful patterns that are important for disease (e.g., cancer) diagnosis and prognosis from complex datasets. This dissertation, organized into four studies, investigates the feasibility of developing several novel ML-based and DL-based CAD schemes for different cancer research purposes. The first study aims to develop and test a unique radiomics-based CT image marker that can be used to detect lymph node (LN) metastasis for cervical cancer patients. A total of 1,763 radiomics features were first computed from the segmented primary cervical tumor depicted on one CT image with the maximal tumor region. Next, a principal component analysis algorithm was applied on the initial feature pool to determine an optimal feature cluster. Then, based on this optimal cluster, machine learning models (e.g., support vector machine (SVM)) were trained and optimized to generate an image marker to detect LN metastasis. The SVM based imaging marker achieved an AUC (area under the ROC curve) value of 0.841 ± 0.035. This study initially verifies the feasibility of combining CT images and the radiomics technology to develop a low-cost image marker for LN metastasis detection among cervical cancer patients. In the second study, the purpose is to develop and evaluate a unique global mammographic image feature analysis scheme to identify case malignancy for breast cancer. From the entire breast area depicted on the mammograms, 59 features were initially computed to characterize the breast tissue properties in both the spatial and frequency domain. Given that each case consists of two cranio-caudal and two medio-lateral oblique view images of left and right breasts, two feature pools were built, which contain the computed features from either two positive images of one breast or all the four images of two breasts. For each feature pool, a particle swarm optimization (PSO) method was applied to determine the optimal feature cluster followed by training an SVM classifier to generate a final score for predicting likelihood of the case being malignant. The classification performances measured by AUC were 0.79±0.07 and 0.75±0.08 when applying the SVM classifiers trained using image features computed from two-view and four-view images, respectively. This study demonstrates the potential of developing a global mammographic image feature analysis-based scheme to predict case malignancy without including an arduous segmentation of breast lesions. In the third study, given that the performance of DL-based models in the medical imaging field is generally bottlenecked by a lack of sufficient labeled images, we specifically investigate the effectiveness of applying the latest transferring generative adversarial networks (GAN) technology to augment limited data for performance boost in the task of breast mass classification. This transferring GAN model was first pre-trained on a dataset of 25,000 mammogram patches (without labels). Then its generator and the discriminator were fine-tuned on a much smaller dataset containing 1024 labeled breast mass images. A supervised loss was integrated with the discriminator, such that it can be used to directly classify the benign/malignant masses. Our proposed approach improved the classification accuracy by 6.002%, when compared with the classifiers trained without traditional data augmentation. This investigation may provide a new perspective for researchers to effectively train the GAN models on a medical imaging task with only limited datasets. Like the third study, our last study also aims to alleviate DL models’ reliance on large amounts of annotations but uses a totally different approach. We propose employing a semi-supervised method, i.e., virtual adversarial training (VAT), to learn and leverage useful information underlying in unlabeled data for better classification of breast masses. Accordingly, our VAT-based models have two types of losses, namely supervised and virtual adversarial losses. The former loss acts as in supervised classification, while the latter loss works towards enhancing the model’s robustness against virtual adversarial perturbation, thus improving model generalizability. A large CNN and a small CNN were used in this investigation, and both were trained with and without the adversarial loss. When the labeled ratios were 40% and 80%, VAT-based CNNs delivered the highest classification accuracy of 0.740±0.015 and 0.760±0.015, respectively. The experimental results suggest that the VAT-based CAD scheme can effectively utilize meaningful knowledge from unlabeled data to better classify mammographic breast mass images. In summary, several innovative approaches have been investigated and evaluated in this dissertation to develop ML-based and DL-based CAD schemes for the diagnosis of cervical cancer and breast cancer. The promising results demonstrate the potential of these CAD schemes in assisting radiologists to achieve a more accurate interpretation of radiological images