Recent Survey on Medical Image Segmentation

Abstract

This chapter presents a survey on the techniques of medical image segmentation. Image segmentation methods are given in three groups based on image features used by the method. The advantages and disadvantages of the existing methods are evaluated, and the motivations to develop new techniques with respect to the addressed problems are given. Digital images and digital videos are pictures and films, respectively, which have been converted into a computer-readable binary format consisting of logical zeros and ones. An image is a still picture that does not change in time, whereas a video evolves in time and generally contains moving and/or changing objects. An important feature of digital images is that they are multidimensional signals, i.e., they are functions of more than a single variable. In the classical study of the digital signal processing the signals are usually one-dimensional functions of time. Images however, are functions of two, and perhaps three space dimensions in case of colored images, whereas a digital video as a function includes a third (or fourth) time dimension as well. A consequence of this is that digital image processing, meaning that significant computational and storage resources are required.

Liver Tumor Segmentation in CT Scans Using Modified SegNet

“…CNN training consumes some time; however, features can be extracted from the trained convolutional network, compared to other complex textural methods. CNNs have proven to be effective in classification tasks [ 26]. The training data and data augmentation are combined by reading batches of tra ining data, applying data augmentation, and sending the augmented data to the training algorithm.…”

“…The supervised method use inputs la beled to train a model for a specific task-liver or tumor segmentation, in this case. On top of these learning methods are the deep learning methods [26,27]. There are many different models of deep learning that have been introduced, such as stacked auto-encoder (SAE), deep belief nets (DBN), convolutional neural networks (CNNs), and Deep Boltzmann Machines (DBM) [28][29][30][31].…”

Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - a review

“…This is a very common method to partition an image where the image background that does not carry any essential information, is removed. Based on the gray level histogram, the threshold value is selected and the difference between the useful and background image pixel intensities segments the image [19]. It is a fast and simple method to implement but does not guarantee object coherency for which post-processing may be required by some other operators.…”

“…[30] that shall be performed before the application of this method. In addition, its requirement for the manual depiction of an initial point makes it disadvantageous [19]. A study in [31] used this method for segmenting out the pectoral tissues from the mammogram and it was further used for classification.…”

Not Just a Matter of Semantics: The Relationship Between Visual and Semantic Similarity

“…There exist applications in which labeled training data cannot be acquired in sufficient amounts to reach the high accuracy associated with contemporary convolutional neural networks (CNNs) with millions of parameters. These include industrial [14,18] and medical [15,27,31] applications as well as research in other fields like wildlife monitoring [4,5,7]. Semantic methods such as knowledge transfer and zero-shot learning process information about the semantic relationship between classes from databases like WordNet [19] to allow high-accuracy classification even when training data is insufficient or missing entirely [24].…”