Abstract
Brain tumors are a major health problem that affect the lives of many people. These tumors are classified as benign or cancerous. The latter can be fatal if not properly diagnosed and treated. Therefore, the diagnosis of brain tumors at the early stages of their development can significantly improve the chances of patient's full recovery after treatment. In addition to laboratory analyses, clinicians and surgeons extract information from medical images, recorded by various systems such as magnetic resonance imaging (MRI), X-ray, and computed tomography (CT). The extracted information is used to identify the essential characteristics of brain tumors (location, size, and type) in order to achieve an accurate diagnosis to determine the most appropriate treatment protocol. In this paper, we present an automated machine vision technique for the detection and localization of brain tumors in MRI images at their very early stages using a combination of k-means clustering, patch-based image processing, object counting, and tumor evaluation. The technique was tested on twenty real MRI images and was found to be capable of detecting multiple tumors in MRI images regardless of their intensity level variations, size, and location including those with very small sizes. In addition to its use for diagnosis, the technique can be integrated into automated treatment instruments and robotic surgery systems.
Highlights
A brain tumor is an abnormal mass of tissues that grows and multiplies rapidly
Magnetic resonance imaging (MRI) systems are capable of producing images of di erent sections within the brain with no overlap of other anatomical structures, which can provide detailed information about brain tumors such as exact location, shape, and size. is information can help clinicians and surgeons to reach an accurate diagnosis of tumors in order to determine the appropriate treatment procedure/protocol such as surgery, chemotherapy, and radiotherapy [3]
We propose an automated technique that can detect and localize multiple brain tumors, including those with very small sizes. e technique begins with an initialization step using k-means clustering to identify the brain surrounding edge, followed by dividing the MRI image into patches that are iteratively scaled, followed by object detection and counting using multiple threshold values
Summary
A brain tumor is an abnormal mass of tissues that grows and multiplies rapidly. E timely detection and diagnosis of these tumors at their very early stages help clinicians to decide the most appropriate treatment protocol. Magnetic resonance imaging (MRI) is one of the most advanced medical imaging modalities It is a noninvasive so tissue contrast imaging used for the diagnosis of tumors within the human brain tissues [2]. Is information can help clinicians and surgeons to reach an accurate diagnosis of tumors in order to determine the appropriate treatment procedure/protocol such as surgery, chemotherapy, and radiotherapy [3]. Manual extraction of the essential clinical information from the MRI images is not an easy task because of the complex nature of these scans, which requires interpretation by skilled and experienced medical professionals. To accelerate the diagnosis process and make it accurate and reliable, various automated segmentation and detection techniques were developed [4]
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