Deep learning has been widely used to segment tumour regions in stained histopathology images. However, precise annotations are expensive and labour-consuming. To reduce the manual annotation workload, we propose a light annotation-based fine-level segmentation approach for histology images based on VGG-based Fusion network with Global Normalisation CAM. The experts only required provide rough ...

For making an image, which is more suitable for segmentation, feature extraction, object recognition, and Human Visual System, image fusion is frequently used technique. It combines complimentary information from different images of the same scene in a single image. In this paper, a simple but efficient algorithm is presented for image fusion employed in wavelet packet domain. For fusion, all t...

Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis, treatment planning, and treatment outcome evaluation. Build upon successful deep learning techniques, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNNs) and Conditional Random Fields (CRFs) in a unified framework to obtain segmentation results ...

In this paper, we propose a real-time system, Hierarchical Feature Selection (HFS), that performs image segmentation at a speed of 50 frames-per-second. We make an attempt to improve the performance of previous image segmentation systems by focusing on two aspects: (1) a careful system implementation on modern GPUs for efficient feature computation; and (2) an effective hierarchical feature sel...

The main objective of image fusion is to combine information from more than one image of the same scene. The resulting image is more suitable for human as well as machine perception or further image-processing tasks as image segmentation, object recognition and feature extraction. In this paper, principal component analysis based and average pixel level base approach is proposed for pixel level...

Today in this digital world Image Fusion is the emerging field in the area of Image Processing. Its main purpose is to combine two or more images in such a way as to keep the most desirable characteristics of each in resultant image. Image fusion techniques can enhance the quality and raise the application of these data. In this paper, we apply image fusion technique on color image as well as b...

The purpose of multi-focus image fusion is gathering the essential information and the focused parts from the input multi-focus images into a single image. These multi-focus images are captured with different depths of focus of cameras. A lot of multi-focus image fusion techniques have been introduced using considering the focus measurement in the spatial domain. However, the multi-focus image ...

A novel and optimal algorithm is presented that is suitable for multifocus image fusion. A synergistic combination of segmentation techniques and genetic search strategies is employed in salience analysis of contrast feature-vision system. Some evaluation measures are suggested and applied to compare the performance of different fusion schemes. Two cases of the generated test images are discuss...

detecting blood vessels is an important task in retinal image analysis.  the task is more challenging with the presence of bright and dark lesions in retinal images. here, a method is proposed to detect vessels in both normal and abnormal retinal fundus images based on their linear features. first, the negative impact of bright lesions is reduced by using k-means segmentation in a perceptive sp...