SRIS: Saliency-Based Region Detection and Image Segmentation of COVID-19 Infected Cases

Abstract

Noise or artifacts in an image, such as shadow artifacts, deteriorate the performance of stateof-the-art models for the segmentation of an image. In this study, a novel saliency-based region detection and image segmentation (SRIS) model is proposed to overcome the problem of image segmentation in the existence of noise and intensity inhomogeneity. Herein, a novel adaptive level-set evolution protocol based on the internal and external functions is designed to eliminate the initialization sensitivity, thereby making the proposed SRIS model robust to contour initialization. In the level-set energy function, an adaptive weight function is formulated to adaptively alter the intensities of the internal and external energy functions based on image information. In addition, the sign of energy function is modulated depending on the internal and external regions to eliminate the effects of noise in an image. Finally, the performance of the proposed SRIS model is illustrated on complex real and synthetic images and compared with that of the previously reported state-of-the-art models. Moreover, statistical analysis has been performed on coronavirus disease (COVID-19) computed tomography images and THUS10000 real image datasets to confirm the superior performance of the SRIS model from the viewpoint of both segmentation accuracy and time efficiency. Results suggest that SRIS is a promising approach for early screening of COVID-19.

An Efficient CNN-Based Hybrid Classification and Segmentation Approach for COVID-19 Detection

“…A Simplified Pulse Coupled Neural Network (SPCNN) is used to segment the COVID-19 and pneumonia CT images. It is based on salient object detection to determine the region of interest [25][26][27]. The settings of the SPCNN are changed in the proposed model to intensity-ofpixel settings.…”

An Efficient CNN-Based Automated Diagnosis Framework from COVID-19 CT Images

“…Adam optimizer is implemented to update the weights of the network depending on the training data. The softmax activation function is used to provide the classification output [25][26][27][28].…”

“…A novel saliency-based region detection algorithm and an active contour segmentation strategy are applied to segment COVID-19 and pneumonia CT images. In image segmentation, saliency refers to a pixel or object appearance in an obvious way among its neighbors and illustrates the unique characteristics of an image [28]. The saliency information can be used to segment the image.…”

“…A new level-set evolution protocol of active contour is designed depending on internal and external energy functions. A new energy function is derived to extract the objects obviously [28][29][30][31][32][33]. We have I:Ω → 2 with I as the input image and Ω as the image domain.…”

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

“…Saliency detection is extensively used to mitigate the complexity of image analysis and speed up the processing time, and it has gained popular applications in the disciplines of computer vision and artificial intelligence [8,10]. The numerous application domains of saliency include image segmentation [11][12][13][14], object detection and recognition [15][16][17], anomaly detection [18,19], image retrieval [20,21], image compression [22], object classification [23], object tracking [24], image retargeting, and summarization [25,26], alpha matting [26], target detection [27], video object segmentation [28], video summarization [29], user perceptions of digital video contents [30], and visual tracking [31]. Countless applications of saliency detection have led to the occurrence of numerous methods for saliency computation.…”