A Novel Fusion Framework Based on Adaptive PCNN in NSCT Domain for Whole-Body PET and CT Images

<div>The average values of CE, IQI, <svg height="11.8174pt" id="M129" style="vertical-align:-3.1815pt" version="1.1" viewbox="-0.0498162 -8.6359 15.6443 11.8174" width="15.6443pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M699 368C699 549 574 666 407 666C186 666 23 488 23 277C23 113 129 -3 288 -13L307 -26C431 -111 501 -139 533 -147C559 -154 613 -163 658 -164L666 -141C597 -111 507 -66 430 -11L416 -1C580 42 699 190 699 368ZM601 371C601 227 518 54 381 22L354 40L278 24C175 47 120 145 120 269C120 451 235 631 398 631C540 631 601 521 601 371Z" id="g113-82"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="722" vert-adv-y="722"></glyph.data></g><g transform="matrix(.0091,0,0,-0.0091,9.321,3.132)"><path d="M615 650H141L132 618C223 612 227 607 212 526L136 123C122 42 112 38 27 32L18 0H522C539 33 580 135 592 166L558 179C536 139 512 100 486 75C454 48 420 41 345 41C287 41 252 42 237 55C221 68 220 100 231 158L261 316H357C455 316 455 309 455 235H489L529 437H494C468 366 460 360 369 360H269L305 565C315 609 316 611 357 611H450C525 611 535 599 548 578C559 556 561 526 560 482L595 486L615 650Z" id="g50-70"></path><glyph.data ascent="3443" descent="-2856" horiz-adv-x="639" vert-adv-y="639"></glyph.data></g></svg>, and <svg height="14.4088pt" id="M130" style="vertical-align:-2.1506pt" version="1.1" viewbox="-0.0498162 -12.2582 31.6028 14.4088" width="31.6028pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M699 368C699 549 574 666 407 666C186 666 23 488 23 277C23 113 129 -3 288 -13L307 -26C431 -111 501 -139 533 -147C559 -154 613 -163 658 -164L666 -141C597 -111 507 -66 430 -11L416 -1C580 42 699 190 699 368ZM601 371C601 227 518 54 381 22L354 40L278 24C175 47 120 145 120 269C120 451 235 631 398 631C540 631 601 521 601 371Z" id="g113-82"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="722" vert-adv-y="722"></glyph.data></g><g transform="matrix(.0091,0,0,-0.0091,9.386,-5.741)"><path d="M722 32C646 41 636 48 622 116L544 496L509 667L472 658L153 123C106 45 93 42 27 32L18 0H259L268 32C178 40 172 48 204 107L272 233H509C523 180 533 137 540 96C548 50 546 39 466 32L457 0H714L722 32ZM502 283H299L448 547H450L502 283Z" id="g50-66"></path><glyph.data ascent="3443" descent="-2856" horiz-adv-x="740" vert-adv-y="740"></glyph.data></g><g transform="matrix(.0091,0,0,-0.0091,15.853,-5.741)"><path d="M598 511C598 627 497 650 401 650H141L131 618C219 611 222 606 210 538L130 119C117 43 108 39 25 32L18 0H236C324 0 396 5 456 30C531 59 585 119 585 193C585 285 520 331 428 351V353C529 373 598 423 598 511ZM502 508C502 418 431 368 330 368H263L298 559C303 584 308 595 317 600C329 608 345 612 369 612C428 612 502 595 502 508ZM483 197C483 95 404 39 309 39C225 39 206 58 219 139L256 330H317C395 330 483 301 483 197Z" id="g50-67"></path><glyph.data ascent="3443" descent="-2856" horiz-adv-x="622" vert-adv-y="622"></glyph.data></g><g transform="matrix(.0091,0,0,-0.0091,21.386,-5.741)"><path d="M381 704H317L48 -163H112L381 704Z" id="g50-48"></path><glyph.data ascent="3443" descent="-2856" horiz-adv-x="429" vert-adv-y="429"></glyph.data></g><g transform="matrix(.0091,0,0,-0.0091,25.294,-5.741)"><path d="M606 650H137L129 618C217 609 221 609 203 518L129 129C115 46 106 40 26 32L18 0H300L307 32C210 40 203 45 218 129L249 305H358C454 305 455 296 457 219H491L531 425H497C465 353 463 350 366 350H259L300 569C306 605 310 611 346 611H444C522 611 535 598 541 578C550 557 553 519 552 485L588 489L606 650Z" id="g50-71"></path><glyph.data ascent="3443" descent="-2856" horiz-adv-x="630" vert-adv-y="630"></glyph.data></g></svg> of the compared methods.</div>

Computational and Mathematical Methods in Medicine

fig7

Figure 7

Figure 7: A Novel Fusion Framework Based on Adaptive PCNN in NSCT Domain for Whole-Body PET and CT Images 