By Jinshan Tang, Sos S. Agaian
Exact imaging of cancerous tissue is a serious step within the struggle to decrease melanoma mortality premiums, and computer-aided detection and prognosis (CAD) applied sciences play a key function. during the last 3 a long time, the sphere of diagnostic melanoma imaging has witnessed a outstanding evolution that has affected nearly each point of study and scientific administration of melanoma. This publication discusses contemporary high-quality examine in key applied sciences utilized in CAD platforms; the eleven chapters conceal kinds of cancers (including pores and skin, breast, prostate, and colon melanoma) and various medical fields (such as biomedicine, imaging, picture processing, trend popularity, and method research) to additional the foremost objectives of present melanoma imaging: supply extra trustworthy disorder characterization during the synthesis of anatomic, practical, and molecular imaging details; refine and optimize imaging features in oncology; determine new imaging modalities and findings, and notice the aptitude use of those concepts; locate extra individualized evaluate of tumor biology, custom-made remedies, and reaction to therapy; strengthen image-processing-based melanoma regulate platforms; and discover imaging features and methods to streamline melanoma drug development. Read more...
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Extra resources for Computer-aided cancer detection and diagnosis : recent advances
Each plot illustrates one SVM; the classifier decision is reached by combining all classifiers. Ensemble learning combines multiple trained classifiers under the assumption that multiple models are better than one if they are diverse. 52 The bootstrap is widely used to estimate the standard error or confidence intervals of an estimate. Bagging is based on the bootstrap technique whereby the predictions on bootstrapped samples are aggregated to form an ensemble hypothesis. Boosting combines the predictions from re-sampled data based on the previous model’s performance such that harder data samples for the system are more likely to be sampled.
Comput. Assisted Tomogr. 24(2), 179–188 (2000). 37. F. M. Vos et al. “A New Visualization Method for Virtual Colonoscopy,” in MICCAI, Springer-Verlag, Berlin (2001). 38. J. , “Reversible projection technique for colon unfolding,” IEEE Trans. Biomed. Eng. 57(12), 2861–2869 (2010). 39. J. , “Colonic polyp segmentation in CT colonography based on fuzzy clustering and deformable models,” IEEE Trans. Med. Imag. 23(11), 1344–1352 (2004). 20 Chapter 1 40. A. Jerebko, M. Franaszek, and R. Summers. “Radon transform based polyp segmentation method for CT colonography computer aided diagnosis,” Radiology 225(P), 257 (2002).
54 Recently, CBIR has shown potential as a diagnostic tool in medical applications. CBIR systems describe images as a set of features directly computed from the images and then categorize the images into several categories. The scale-invariant feature transform (SIFT) was first proposed by Lowe55 in the applications of natural-scene and facial recognition. It has the advantage of describing the local image feature with a scale- and rotation-invariant representation. The bag-of-words (BoW) model56 was first introduced in natural language processing then in computer vision, especially for object categorization.
Computer-aided cancer detection and diagnosis : recent advances by Jinshan Tang, Sos S. Agaian