Intelligent image processing techniques for cancer progression detection, recognition and prediction in the human liver

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外文摘要：Clinical Decision Support (CDS) aids in early diagnosis of liver cancer， a potentially fatal disease prevalent in both developed and developing countries。 Our research aims to develop a robust and intelligent clinical decision support framework for disease management of cancer based on legacy Ultrasound (US) image data collected during various stages of liver cancer。 The proposed intelligent CDS framework will automate real-time image enhancement， segmentation， disease classification and progression in order to enable efficient diagnosis of cancer patients at early stages。 The CDS framework is inspired by the human interpretation of US images from the image acquisition stage to cancer progression prediction。 Specifically， the proposed framework is composed of a number of stages where images are first acquired from an imaging source and pre-processed before running through an image enhancement algorithm。 The detection of cancer and its segmentation is considered as the second stage in which different image segmentation techniques are utilized to partition and extract objects from the enhanced image。 The third stage involves disease classification of segmented objects， in which the meanings of an investigated object are matched with the disease dictionary defined by physicians and radiologists。 In the final stage; cancer progression， an array of US images is used to evaluate and predict the future stages of the disease。 For experiment purposes， we applied the framework and classifiers to liver cancer dataset for 200 patients。 Class distributions are 120 benign and 80 malignant in this dataset。

外文关键词：

biomedical ultrasonicscancerdecision support systemsfeature extractionimage enhancementimage segmentationlivermedical image processingobject detectionobject recognitioncancer progression detectioncancer progression predictioncancer recognitionclinical decision supportdisease classificationdisease progressionfatal diseasehuman liverimage acquisitionimage segmentationintelligent CDS frameworkintelligent image processing techniquelegacy ultrasound imageliver cancerreal-time image enhancementCancerClassification algorithmsDiseasesHistogramsImage segmentationLiverSupport vector machinesCDSClassificationImage SegmentationLESHLiver CancerSVMUltrasoundWEKA

作者：

Ali, L.、Hussain, Amir、Li, Jie、Shah, Aamer、Sudhakr, U.、Mahmud, Mufti、Zakir, U.、Yan, Xiaodong、Luo, B.、Rajak, M.

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作者单位：

COSIPRA Lab., Univ. of Stirling, Stirling, UK

会议名称：

IEEE Symposium on Computational Intelligence in Healthcare and e-health

会议地点：

Orlando, FL(US)

会议母体文献：

2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health

页码：

25-31

出版时间：

2014

DOI：

10.1109/CICARE.2014.7007830