Breast Cancer Detection Using Polynomial Fitting for Intensity Spreading Within ROIs

Mostafa, Shaimaa; Mubarak, Roaa I.; Eladawy, Mohamed

doi:10.1007/978-3-319-64861-3_12

Cited by 2 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pre-processing: Applying the step of preprocessing plays a vital role to remove artifacts, labels, and increase the quality of the image [17]. The pre-processing of the images is prepared to reduce the computational rate and exploit the probability of accuracy [17]. It also involves denoising and improving the contrast of the images by removing artifacts.…”

Section: Quantitative Mathematical Objective Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative mathematical objective evaluation of contrast-enhanced spectral mammogram in the assessment of response to neoadjuvant chemotherapy and prediction of residual disease in breast cancer

Moustafa

Kamal

Gomaa

et al. 2019

Egypt J Radiol Nucl Med

Self Cite

View full text Add to dashboard Cite

Background: The aim of the study is to initiate a new quantitative mathematical objective tool for evaluation of response to neoadjuvant chemotherapy (NAC) and prediction of residual disease in breast cancer using contrastenhanced spectral mammography (CESM). Forty-two breast cancer patients scheduled for receiving NAC were included. All patients underwent two CESM examinations: pre and post NAC. To assess the response to neoadjuvant chemotherapy, we used a mathematical image analysis software that can calculate the difference in the intensity of enhancement between the pre and post neoadjuvant contrast images (MATLAB and Simulink) (Release 2013b). The proposed technique used the pre and post neoadjuvant contrast images as inputs. The technique consists of three main steps: (1) preprocessing, (2) extracting the region of interest (ROI), and (3) assessment of the response to chemotherapy by measuring the percentage of change in the intensity of enhancement of malignant lesions in the pre and post neoadjuvant CESM studies using a quantitative mathematical technique. This technique depends on the analysis of number of pixels included within the ROI. We compared this technique with the currently used method of evaluation: RECIST 1.1 (response evaluation criteria in solid tumors 1.1) and using another combined response evaluation approach using both RECIST 1.1 in addition to a subjective visual evaluation. Results were then correlated with the postoperative pathology evaluation using Miller-Payne grades. For statistical evaluation, patients were classified into responders and non-responders in all evaluation methods.

show abstract

Section: Quantitative Mathematical Objective Evaluationmentioning

confidence: 99%

“…It also involves denoising and improving the contrast of the images by removing artifacts. This is followed by resizing all the images to a fixed size [17][18][19][20]. Input images were converted to grayscale intensity image.…”

Section: Quantitative Mathematical Objective Evaluationmentioning

confidence: 99%