Modified Global Flower Pollination Algorithm‐based image fusion for medical diagnosis using computed tomography and magnetic resonance imaging

Abstract

Recently, the computed tomography (CT) and magnetic resonance imaging (MRI) medical image fusion have turned into a challenging issue in the medical field. The optimal fused image is a significant component to detect the disease easily. In this research, we propose an iterative optimization approach for CT and MRI image fusion. Initially, the CT and MRI image fusion is subjected to a multilabel optimization problem. The main aim is to minimize the data and smoothness cost during image fusion. To optimize the fusion parameters, the Modified Global Flower Pollination Algorithm is proposed. Here, six sets of fusion images with different experimental analysis are evaluated in terms of different evaluation metrics such as accuracy, specificity, sensitivity, SD, structural similarity index, feature similarity index, mutual information, fusion quality, and root mean square error (RMSE). While comparing to state‐of‐art methods, the proposed fusion model provides best RMSE with higher fusion performance. Experiments on a set of MRI and CT images of medical data set show that the proposed method outperforms a very competitive performance in terms of fusion quality.

Robust Visual Tracking Based on a Modified Flower Pollination Algorithm

“…,  represents the step size adjustment factor for the search process and parameter L represents pollination intensity, which follows the Levy distribution and its expression is as follows [34]:…”

“…where ) (  represents the standard gamma function, and  takes the value 1.5 [34]. 5) Calculation of the optimal solution and fitness value of the updated population.…”

A hybrid optimized multi-population flower pollination algorithm for web service composition problem

“…Although some FPA algorithm research has achieved good results in most optimization problems [14][15][16], there are still problems such as easy to fall into local optimum [17][18], the low search precision, and insufficient development ability [19]. To solve more optimization problems, many scholars improve the performance of FPA mainly from 5 aspects, the improvement of the initial population [20][21], the improvement in population diversity [22][23], the improvement in parameter settings [24][25], the improvement in search capabilities [26][27] and designing hybrid algorithms [28][29].…”

A Fast Hybrid Classification Algorithm with Feature Reduction for Medical Images

“…Color and shape alteration of the nuclei and cytoplasm can implicate the occurrence of Papilloma virus that causes cervical cancer [ 3 , 4 ]. Manual Pap smear testing is slow and error-prone procedure and requires pathology experts [ 5 , 6 ]. It was found that a lot of inconsistencies from the manual test can compromise the validity the Pap smear process [ 7 ].…”