Computer Aided Diagnosis System for Breast Cancer Detection
01 Jan 2015-pp 1002-1019
TL;DR: Compared to existing multiscale enhancement approaches, images processed with this method appear more familiar to radiologists and naturally close to the original mammogram.
Abstract: Management of breast cancer in elder patients is challenging due to a lack of good quality evidence regarding the role of adjuvant chemotherapy. Mammograms can depict most of the significant changes of breast disease. The primary radiographic signs of breast cancer are masses (its density, site, shape, borders), spicular lesions and calcification content. The basic idea is to convert the mammogram image and convert into 3-D matrix. Obtained matrix is used to convert the mammogram into binary image. Several techniques like detecting cell, filling gaps, dilating gaps, removing border, smoothing the objects, finding structures & extracting large objects have been used. Finally finding the granulometry of tissues in an Image without explicitly segmenting (detecting) each object. Compared to existing multiscale enhancement approaches, images processed with this method appear more familiar to radiologists and naturally close to the original mammogram.
Citations
More filters
01 Jan 2015
TL;DR: The presented service revolves around the use of two wearable inertial sensors, i.e. SensFoot and SensHand, for measuring foot and hand performance in the MDS-UPDRS III motor exercises to identify pre-motor/pre-clinical diagnosis and to provide a complete service of tele-health with remote control provided by cloud technologies.
Abstract: The objective of this chapter is to demonstrate the technical feasibility and medical effectiveness of personalised services and care programmes for Parkinson’s disease, based on the combination of mHealth applications, cooperative ICTs, cloud technologies and wearable integrated devices, which empower patients to manage their health and disease in cooperation with their formal and informal caregivers, and with professional medical staff across different care settings, such as hospital and home. The presented service revolves around the use of two wearable inertial sensors, i.e. SensFoot and SensHand, for measuring foot and hand performance in the MDS-UPDRS III motor exercises. The devices were tested in medical settings with eight patients, eight hyposmic subjects and eight healthy controls, and the results demonstrated that this approach allows quantitative metrics for objective evaluation to be measured, in order to identify pre-motor/pre-clinical diagnosis and to provide a complete service of tele-health with remote control provided by cloud technologies.
15 citations
01 Jan 2016
TL;DR: Therapists, parents and developers of AAC applications must work collaboratively to expand the research pertaining to the assessment and treatment of children who utilize AAC mobile technologies for communication purposes.
Abstract: With the increased development of mobile technologies, such as smartphones and tablets (i.e. iPhone, iPad), the field of augmentative and alternative communication (AAC) has changed rapidly over the last few years. Recent advances in technology have introduced applications (apps) for AAC purposes. These novel technologies could provide numerous benefits to individuals with complex communication needs. Nevertheless, introducing mobile technology apps is not without risk. Since these apps can be purchased and retrieved with relative ease, AAC assessments and collaborative evaluations have been circumvented in favor of the “quick fix”-simply ordering a random app for a potential user, without fully assessing the individual’s needs and abilities. There is a paucity of research pertaining to mobile technology use in AAC. Therapists, parents and developers of AAC applications must work collaboratively to expand the research pertaining to the assessment and treatment of children who utilize AAC mobile technologies for communication purposes.
2 citations
References
More filters
TL;DR: A novel algorithm for image denoising and enhancement based on dyadic wavelet processing is proposed, which seems to meaningfully improve the diagnosis in the early breast cancer detection with respect to other approaches.
Abstract: Mammography is the most effective method for the early detection of breast diseases. However, the typical diagnostic signs such as microcalcifications and masses are difficult to detect because mammograms are low-contrast and noisy images. In this paper, a novel algorithm for image denoising and enhancement based on dyadic wavelet processing is proposed. The denoising phase is based on a local iterative noise variance estimation. Moreover, in the case of microcalcifications, we propose an adaptive tuning of enhancement degree at different wavelet scales, whereas in the case of mass detection, we developed a new segmentation method combining dyadic wavelet information with mathematical morphology. The innovative approach consists of using the same algorithmic core for processing images to detect both microcalcifications and masses. The proposed algorithm has been tested on a large number of clinical images, comparing the results with those obtained by several other algorithms proposed in the literature through both analytical indexes and the opinions of radiologists. Through preliminary tests, the method seems to meaningfully improve the diagnosis in the early breast cancer detection with respect to other approaches.
203 citations
TL;DR: A computer aided neural network classification of regions of suspicion (ROS) on digitized mammograms is presented in this paper which employs features extracted by a new technique based on independent component analysis.
141 citations
"Computer Aided Diagnosis System for..." refers background in this paper
...Christoyianni et al. (2002) mentioned that the chance that breast cancer will be responsible for a woman’s death is about 1 in 35 (about 3%), although breast cancer has very high incidence and death rate, the cause of breast cancer is still unknown....
[...]
TL;DR: A preliminary clinical evaluation at TaiChung Veterans General Hospital (TCVGH) has shown that the system is very flexible and can be integrated with the existing Picture Archiving and Communications System (PACS) currently implemented in the Department of Radiology at TCVGH.
66 citations
TL;DR: There is interest among oncologists who responded to the survey to validate a comprehensive geriatric assessment for use as a predictive instrument of toxicity and/or activity of anticancer therapy and to evaluate the role of a treatment option that is potentially less toxic and possibly as effective as polychemotherapy.
52 citations
TL;DR: The current state of the ongoing the BC automated diagnosis research program is described, a software system that provides expert diagnosis of breast cancer based on three step of cytological image analysis based on segmentation using an active contour for cell tracking and isolating of the nucleus in the studied image.
Abstract: Breast cancer accounts for the second most cancer diagnoses among women and the second most cancer deaths in the world. In fact, more than 11000 women die each year, all over the world, because this disease. The automatic breast cancer diagnosis is a very important purpose of medical informatics researches. Some researches has been oriented to make automatic the diagnosis at the step of mammographic diagnosis, some others treated the problem at the step of cytological diagnosis. In this work, we describes the current state of the ongoing the BC automated diagnosis research program. It is a software system that provides expert diagnosis of breast cancer based on three step of cytological image analysis. The first step is based on segmentation using an active contour for cell tracking and isolating of the nucleus in the studied image. Then from this nucleus, have been extracted some textural features using the wavelet transforms to characterize image using its texture, so that malign texture can be differentiated from benign on the assumption that tumoral texture is different from the texture of other kinds of tissues. Finally, the obtained features will be introduced as the input vector of a Multi-Layer Perceptron (MLP), to classify the images into malign and benign ones.
15 citations