Original Research ArticleA novel privacy-supporting 2-class classification technique for brain MRI images
Introduction
The growth rate of brain diseases among children and adults are increasing exponentially day-by-day. Hence quick and accurate detection of brain disease can be very helpful to maintain a healthy lifestyle. The Electronic Health Records (EHR) of the patients automate the access to important healthcare information and can improve patient care by streamlining the clinical process. A varieties of medical imaging techniques, viz., Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI) produce visual depiction of the internal substances of brain that helps in proper medical diagnosis. MRI is more significant and suitable than other imaging tools due to its superior contrast, non-invasive, and non-radiation properties [1]. In addition to that, different soft tissues, white, and gray matters of the brain are more clearly visible via MRI vis-a-vis other brain imaging techniques.
Manual segmentation becomes time consuming and tedious for large pool of brain MR images [2]. This necessitates the design of Computer Aided Diagnosis (CAD) systems for assisting radiologists [3] to accurately classify brain MR images (symmetry-asymmetry detection). At the same time, CAD can reduce the burden of increasing workload for the medical personnel, also supports in improving the classification accuracy by implementing some machine learning techniques [4]. CAD is a diagnostic system which integrates various image processing techniques like feature extraction, selection, and classification of images for the pathological brain detection. Moreover, it is a tough task to devise an accurate and efficient CAD system regardless of the size and the quality of the brain MR images. CAD systems are primarily designed for improving maximum possible accuracy while classifying brain MR images.
Various studies on automated CAD system categorize it into two basic types, viz., a. CAD systems related to classify brain MR images, b. CAD systems to differentiate between benign and malign diseases. The main objective behind this is to design a fast and reliable CAD system to enhance diagnostic accuracy of radiologists and at the same time reducing the burden of increasing workload. To improve the accuracy of the CAD systems, correct amalgamation of feature extractor, selector, and classifier plays a crucial role. Moreover, all imaging modalities contain patients’ private information. Hence, privacy issues, in addition to classification accuracy are also critical and an open challenge when a healthcare provider plans to deploy an online medical record management system. In Internet of Medical Things (IoMT), privacy and confidentiality of patient's sensitive information must be preserved and only accessible to authorized parties [5]. Therefore, it is essential to develop a relatable CAD system to achieve clinically acceptable classification accuracy with reduced feature count along with addressing such privacy issues and challenges. Motivated by the above prime requirements, we devise automated CAD systems (with a novel combination of extractor, selector, and classifier) to assist the radiologists for quicker and easier diagnosis of brain diseases without compromising privacy of patients’ sensitive information.
The article is organized as follows. Section 2 provides some of the existing brain MR image classification methods. Section 3 describes two new proposed frameworks for implementing privacy supporting two class classifiers. Section 4 demonstrates the experimental analysis and comparative result of both proposed frameworks on three different datasets. Finally, Section 5, presents the importance of our proposed techniques with an outline of possible future augmentations to this exploration.
Section snippets
Related work
The state-of-the-art research challenges related to CAD and brain MR image classifier systems have been reviewed and presented in this section. In the past years, a significant amount of work has been done in segregating normal brain from pathological brain. In almost all Pathological Brain Detection Systems (PBDSs), MRI has been extensively used as an imaging technique, as it provides ample amount of information about different soft tissues of the brain. Wavelet Transform is the most
Proposed methodologies
Different researchers have tried different combinations of feature extraction, selection, and classification mechanisms to find satisfactory accuracy in brain MR image classification. With a quest to have equivalent classification, we have used DWT as feature extractor, SUR and Probabilistic Principal Components Analysis (PPCA) as feature selectors, and SVM and Filtered Classifier (FC) as 2-class classifiers. Further, we have employed handcrafted features since these feature vectors are best
Datasets used
We have reviewed four brain MR image datasets from three different sources. The details on all these datasets are described in Table 1.
The 3D dataset Harvard GSP [26] sourced from Harvard dataverse contains 1500 brain genomics of T1-weighted MRI. These datasets are primarily used for medical image registration [25], and have 256 × 256 × 256 in-plane resolutions. So, this dataset is not suitable for our experiments. To initially verify the performance of brain abnormality classification, we have
Conclusion
In this work, we propose two new privacy supporting binary classifier systems for automated detection of pathological brain in MR images. The novelty of our work lies in guaranteeing the privacy of patients EHR without much affecting the classification rate. We adopt LSB substitution steganographic method and visually hide private information of a patient by watermarking it on the original MR images itself prior to classification. We focus on achieving better classification rate towards
Acknowledgment
This research is partially supported by the following project titled "Deep learning applications for computer vision task" funded by NITROAA with support of Lenovo P920 workstation and NVIDIA Corporation with support of NVIDIA Titan V GPU.
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