A New Multistage Medical Segmentation Method Based on Superpixel and Fuzzy Clustering

The medical image segmentation is the key approach of image processing for brain MRI images. However, due to the visual complex appearance of image structures and the imaging characteristic, it is still challenging to automatically segment brain MRI image. A new multi-stage segmentation method based on superpixel and fuzzy clustering (MSFCM) is proposed to achieve the good brain MRI segmentation results. The MSFCM utilizes the superpixels as the clustering objects instead of pixels, and it can increase the clustering granularity and overcome the influence of noise and bias effectively. In the first stage, the MRI image is parsed into several atomic areas, namely, superpixels, and a further parsing step is adopted for the areas with bigger gray variance over setting threshold. Subsequently, designed fuzzy clustering is carried out to the fuzzy membership of each superpixel, and an iterative broadcast method based on the Butterworth function is used to redefine their classifications. Finally, the segmented image is achieved by merging the superpixels which have the same classification label. The simulated brain database from BrainWeb site is used in the experiments, and the experimental results demonstrate that MSFCM method outperforms the traditional FCM algorithm in terms of segmentation accuracy and stability for MRI image

Accurate segmentation and registration of skin lesion images to evaluate lesion change

Skin cancer is a major health problem. There are several techniques to help diagnose skin lesions from a captured image. Computer-aided diagnosis (CAD) systems operate on single images of skin lesions, extracting lesion features to further classify them and help the specialists. Accurate feature extraction, which later on depends on precise lesion segmentation, is key for the performance of these systems. In this paper, we present a skin lesion segmentation algorithm based on a novel adaptation of superpixels techniques and achieve the best reported results for the ISIC 2017 challenge dataset. Additionally, CAD systems have paid little attention to a critical criterion in skin lesion diagnosis: the lesion's evolution. This requires operating on two or more images of the same lesion, captured at different times but with a comparable scale, orientation, and point of view; in other words, an image registration process should first be performed. We also propose in this work, an image registration approach that outperforms top image registration techniques. Combined with the proposed lesion segmentation algorithm, this allows for the accurate extraction of features to assess the evolution of the lesion. We present a case study with the lesion-size feature, paving the way for the development of automatic systems to easily evaluate skin lesion evolutionThis work was supported in part by the Spanish Government (HAVideo, TEC2014-53176-R) and in part by the TEC department (Universidad Autonoma de Madrid

Breast density segmentation based on fusion of super pixels and watershed transform

Breast density, defined as the proportion of fibroglandular tissue over the entire breast has been linked with a higher risk of developing breast cancer, in fact it has been suggested that women with a mammographic breast density higher than 75 percent have a four-to six-fold higher risk of developing breast cancer than women with little or no dense tissue. Therefore, automatic methods of measuring breast density could potentially aid clinicians to provide more precise breast cancer risk estimates.This paper proposes a novel method of segmenting breast density, which extracts objects with the same density using fusion of super pixels and a watershed based technique, this idea is based on the principle that both super pixel and watershed often results in over segmentation, for the later algorithm, over segmentation may be due to contours which have been suppressed according to similarity of contrast and topological measures, we took advantage of super pixel to consolidate space information and efficiently process the intensity non-homogeneity problem, afterward, re-introduced this contour with watershed transform to get a better segmentation.authorsversionPeer reviewe

A Robust SVM Color-Based Food Segmentation Algorithm for the Production Process of a Traditional Carasau Bread

In this paper, we address the problem of automatic image segmentation methods applied to the partial automation of the production process of a traditional Sardinian flatbread called pane Carasau for assuring quality control. The study focuses on one of the most critical activities for obtaining an efficient degree of automation: the estimation of the size and shape of the bread sheets during the production phase, to study the shape variations undergone by the sheet depending on some environmental and production variables. The knowledge can thus be used to create a system capable of predicting the quality of the shape of the dough produced and empower the production process. We implemented an image acquisition system and created an efficient machine learning algorithm, based on support vector machines, for the segmentation and estimation of image measurements for Carasau bread. Experiments demonstrated that the method can successfully achieve accurate segmentation of bread sheets images, ensuring that the dimensions extracted are representative of the sheets coming from the production process. The algorithm proved to be fast and accurate in estimating the size of the bread sheets in various scenarios that occurred over a year of acquisitions. The maximum error committed by the algorithm is equal to the 2.2% of the pixel size in the worst scenario and to 1.2% elsewhere

Automatic skin lesion segmentation by coupling deep fully convolutional networks and shallow network with textons

Segmentation of skin lesions is an important step in computer-aided diagnosis of melanoma; it is also a very challenging task due to fuzzy lesion boundaries and heterogeneous lesion textures. We present a fully automatic method for skin lesion segmentation based on deep fully convolutional networks (FCNs). We investigate a shallow encoding network to model clinically valuable prior knowledge, in which spatial filters simulating simple cell receptive fields function in the primary visual cortex (V1) is considered. An effective fusing strategy using skip connections and convolution operators is then leveraged to couple prior knowledge encoded via shallow network with hierarchical data-driven features learned from the FCNs for detailed segmentation of the skin lesions. To our best knowledge, this is the first time the domain-specific hand craft features have been built into a deep network trained in an end-to-end manner for skin lesion segmentation. The method has been evaluated on both ISBI 2016 and ISBI 2017 skin lesion challenge datasets. We provide comparative evidence to demonstrate that our newly designed network can gain accuracy for lesion segmentation by coupling the prior knowledge encoded by the shallow network with the deep FCNs. Our method is robust without the need for data augmentation or comprehensive parameter tuning, and the experimental results show great promise of the method with effective model generalization compared to other state-of-the-art-methods

Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions

Segmentation is one of the most essential steps required to identify the inert object in the chest x-ray. A review with the existing segmentation techniques towards chest x-ray as well as other vital organs was performed. The main objective was to find whether existing system offers accuracy at the cost of recursive and complex operations. The proposed system contributes to introduce a framework that can offer a good balance between computational performance and segmentation performance. Given an input of chest x-ray, the system offers progressive search for similar image on the basis of similarity score with queried image. Region-based shape descriptor is applied for extracting the feature exclusively for identifying the lung region from the thoracic region followed by contour adjustment. The final segmentation outcome shows accurate identification followed by segmentation of apical and costophrenic region of lung. Comparative analysis proved that proposed system offers better segmentation performance in contrast to existing system

Information Extraction from Messy Data, Noisy Spectra, Incomplete Data, and Unlabeled Images

Data collected from real-world scenarios are never ideal but often messy because data errors are inevitable and may occur in creative and unexpected ways. And there are always some unexpected tricky troubles between ideal theory and real-world applications. Although with the development of data science, more and more elegant algorithms have been well developed and validated by rigorous proof, data scientists still have to spend 50\% to 80\% of their work time on cleaning and organizing data, leaving little time for actual data analysis. This dissertation research involves three scenarios of statistical modeling with common data issues: quantifying function effect on noisy functional data, multistage decision-making model over incomplete data, and unsupervised image segmentation over imperfect engineering images. And three methodologies are proposed accordingly to solve them efficiently. In Chapter 2, a general two-step procedure is proposed to quantify the effects of a certain treatment on the spectral signals subjecting to multiple uncertainties for an engineering application that involves materials treatment for aircraft maintenance. With this procedure, two types of uncertainties in the spectral signals, offset shift and multiplicative error, are carefully addressed. In the two-step procedure, a novel optimization problem is formulated to estimate the representative template spectrum first, and then another optimization problem is formulated to obtain the pattern of modification $\mathbf{g}$ that reveals how the treatment affects the shape of the spectral signal, as well as a vector $\boldsymbol{\delta}$ that describes the degree of change caused by different treatment magnitudes. The effectiveness of the proposed method is validated in a simulation study. \textcolor{black}{Furtherly, in} a real case study, the proposed method \textcolor{black}{is used} to investigate the effect of plasma exposure on the FTIR spectra. As a result, the proposed method effectively identifies the pattern of modification under uncertainties in the manufacturing environment, which matches the knowledge of the affected chemical components by the plasma treatment. And the recovered magnitude of modification provides guidance in selecting the control parameter of the plasma treatment. In Chapter 3, an active learning-based multistage sequential decision-making model is proposed to assist doctors and patients to make cost-effective treatment recommendations when some clinical data are more expensive or time-consuming to collect than other laboratory data. The main idea is to formulate the incomplete clinical data into a multistage decision-making model where the doctors can make diagnostics decisions sequentially in these stages, and actively collect only the necessary examination data from certain patients rather than all. There are two novelties in estimating parameters in the proposed model. First, unlike the existed ordinal logistic regression model which only models a single stage, a multistage model is built by maximizing the joint likelihood function for all samples in all stages. Second, considering that the data in different stages are nested in a cumulative way, it is assumed that the coefficients for common features in different stages are invariant. Compared with the baseline approach that models each stage individually and independently, the proposed multistage model with common coefficients assumption has significant advantages. It reduces the number of variables to estimate significantly, improves the computational efficiency, and makes the doctors feel intuitive by assuming that newly added features will not affect the weights of existed ones. In a simulation study, the relative efficiency of the proposed method with regards to the baseline approach is 162\% to 1,938\%, proving its efficiency and effectiveness soundly. Then, in a real case study, the proposed method estimates all parameters very efficiently and reasonably. %It estimates all parameters simultaneously to reach the global optimum and fully considers the cumulative characteristics between these stages by making common coefficients assumption. In Chapter 4, a simple yet very effective unsupervised image segmentation method, called RG-filter, is proposed to segment engineering images with no significant contrast between foreground and background for a material testing application. With the challenge of limited data size, imperfect data quality, unreachable binary true label, we developed the RG-filter which thresholding the pixels according to the relative magnitude of the R channel and G channel of the RGB image. %And the other one is called the superpixels clustering algorithm, where we add another layer of clustering over the segmented superpixels to binarize their labels. To test the performance of the existed image segmentation and proposed algorithm on our CFRP image data, we conducted a series of experiments over an example specimen. Comparing all the pixel labeling results, the proposed RG-filter outperforms the others to be the most recommended one. in addition, it is super intuitive and efficient in computation. The proposed RG-filter can help to analyze the failure mode distribution and proportion on the surface of composite material after destructive DCB testing. The result can help engineers better understand the weak link during the bonding of composite materials, which may provide guidance on how to improve the joining of structures during aircraft maintenance. Also, it can be crucial data when modeling together with some downstream data as a whole. And if we can predict it from other variables, the destructive DCB testing can be avoided, a lot of time and money can be saved. In Chapter 5, we concluded the dissertation and summarized the original contributions. In addition, future research topics associated with the dissertation have also been discussed. In summary, the dissertation contributes to the area of \textit{System Informatics and Control} (SIAC) to develop systematic methodologies based on messy real-world data in the field of composite materials and healthcare. The fundamental methodologies developed in this thesis have the potential to be applied to other advanced manufacturing systems.Ph.D

A Review Of Vision Based Defect Detection Using Image Processing Techniques For Beverage Manufacturing Industry

Vision based quality inspection emerged as a prime candidate in beverage manufacturing industry. It functions to control the product quality for the large scale industries; not only to save time, cost and labour, but also to secure a competitive advantage. It is a requirement of International Organization for Standardization (ISO) 9001, to appease the customer satisfaction in term of frequent improvement of the quality of products and services. It is totally impractical to rely on human inspector to handle a large scale quality control production because human has major drawback in their performance such as inconsistency and time consuming. This article reviews defect detection using image processing techniques for beverage manufacturing industry. There are comparative studies on techniques suggested by previous researchers. This review focuses on shape defect detection, color concentration inspection and level of liquid products measurement in a container. Shape, color and level defects are the main concern for bottle inspection in beverage manufacturing industry. The development of practical testing and the services performance are also discussed in this paper