Feature-enhanced fusion of U-NET-based improved brain tumor images segmentation

Abstract

AbstractThe field of medical image segmentation, particularly in the context of brain tumor delineation, plays an instrumental role in aiding healthcare professionals with diagnosis and accurate lesion quantification. Recently, Convolutional Neural Networks (CNNs) have demonstrated substantial efficacy in a range of computer vision tasks. However, a notable limitation of CNNs lies in their inadequate capability to encapsulate global and distal semantic information effectively. In contrast, the advent of Transformers, which has established their prowess in natural language processing and computer vision, offers a promising alternative. This is primarily attributed to their self-attention mechanisms that facilitate comprehensive modeling of global information. This research delineates an innovative methodology to augment brain tumor segmentation by synergizing UNET architecture with Transformer technology (denoted as UT), and integrating advanced feature enhancement (FE) techniques, specifically Modified Histogram Equalization (MHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Modified Bi-histogram Equalization Based on Optimization (MBOBHE). This integration fosters the development of highly efficient image segmentation algorithms, namely FE1-UT, FE2-UT, and FE3-UT. The methodology is predicated on three pivotal components. Initially, the study underscores the criticality of feature enhancement in the image preprocessing phase. Herein, techniques such as MHE, CLAHE, and MBOBHE are employed to substantially ameliorate the visibility of salient details within the medical images. Subsequently, the UT model is meticulously engineered to refine segmentation outcomes through a customized configuration within the UNET framework. The integration of Transformers within this model is instrumental in imparting contextual comprehension and capturing long-range data dependencies, culminating in more precise and context-sensitive segmentation. Empirical evaluation of the model on two extensively acknowledged public datasets yielded accuracy rates exceeding 99%.