Segmentation and Assessment of Leukocytes Using Entropy-Based Procedure

Manasi, Susmita Dey; Ramyaa, M.; Raja, N. Sri Madhava; Arunmozhi, S.; Satapathy, Suresh Chandra

doi:10.1007/978-981-15-5679-1_67

Cited by 1 publication

(3 citation statements)

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“…This method was evaluated on datasets with single cell-based leukocyte images [11]. In another method, WBC assessment and segmentation are carried out by undergoing entropy-based procedures [12]. To improve the segmentation performance, a bi-level threshold is used in this approach [12].…”

Section: A Wbc Segmentation Based On Handcraftedfeaturesmentioning

confidence: 99%

“…In another method, WBC assessment and segmentation are carried out by undergoing entropy-based procedures [12]. To improve the segmentation performance, a bi-level threshold is used in this approach [12]. Moreover, experiments are performed on a single famous leukocytes dataset for evaluation of their method [12].…”

Section: A Wbc Segmentation Based On Handcraftedfeaturesmentioning

confidence: 99%

“…To improve the segmentation performance, a bi-level threshold is used in this approach [12]. Moreover, experiments are performed on a single famous leukocytes dataset for evaluation of their method [12]. In a study, the nucleus and cytoplasm of WBC are segmented using image texture and color-based enhancements [13].…”

Section: A Wbc Segmentation Based On Handcraftedfeaturesmentioning

confidence: 99%

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Exploiting the Multiscale Information Fusion Capabilities for Aiding the Leukemia Diagnosis Through White Blood Cells Segmentation

et al. 2022

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Leukemia is one of the most terminal types of blood cancer, and many people suffer from it every year. White blood cells (WBCs) have a significant association with leukemia diagnosis. Research studies reported that leukemia brings changes in WBC count and morphology. WBC accurate segmentation enables to detect morphology and WBC count which consequently helps in the diagnosis and prognosis of leukemia. Manual WBC assessment methods are tedious, subjective, and less accurate. To overcome these problems, we propose a multi-scale information fusion network (MIF-Net) for WBC segmentation. MIF-Net is a shallow architecture with internal and external spatial information fusion mechanisms. In WBC images, the cytoplasm is with low contrast compared to the background, whereas nuclei shape can be complex with an indistinctive boundary for some cases, therefore accurate segmentation becomes challenging. Spatial features in the initial layers of the network include fine boundary information and MIF-Net splits and propagates this boundary information on multi-scale for external information fusion. Multi-scale information fusion in our network helps in preserving boundary information and contributes to segmentation performance improvement. MIF-Net also uses internal information fusion after intervals for feature empowerment in different stages of the network. We evaluated our network for four publicly available datasets and achieved state-of-the-art segmentation performance. In addition, the proposed architecture exhibits superior computational efficiency by using only 2.67 million trainable parameters.

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Section: A Wbc Segmentation Based On Handcraftedfeaturesmentioning

confidence: 99%